notes

summary notes and more

summary_notes.csv

"arxiv_code","level","summary","tokens"
"1409.4842",1,"- Introduction of Inception, a deep convolutional neural network architecture, responsible for setting new state-of-the-art in ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
- Improved utilization of computing resources within the network achieved through a carefully crafted design that allows for increasing depth and width while maintaining constant computational budget.
- Architectural decisions based on Hebbian principle and multi-scale processing intuition.
- GoogLeNet, a 22-layer deep network used in ILSVRC14 submission, assessed for classification and detection accuracy.
- Significant progress in image recognition and object detection due to new ideas, algorithms, and improved architectures rather than just hardware or dataset size.
- GoogLeNet uses 12x fewer parameters compared to Krizhevsky et al.'s winning architecture from two years prior while being more accurate.
- Synergy of deep architectures and classical computer vision techniques like R-CNN algorithm by Girshick et al. for object detection improvements.
- Consideration of efficiency (power, memory use) in the design of Inception architecture due to mobile and embedded computing traction.
- Models designed to maintain a computational budget of 1.5 billion multiply-adds at inference time, making them suitable for real-world use even on large datasets.
- Efficient deep neural network architecture for computer vision applications presented in the paper.
- Introduction of Inception, a deep neural network architecture for computer vision, combining the Network in Network concept and ""Inception module"" to increase depth and width without performance penalty.
- Inception model outperforms state-of-the-art on ILSVRC 2014 classification and detection challenges.
- Related work: Convolutional neural networks (CNNs) typically have stacked convolutional layers, followed by fully-connected layers. Increasing network depth and layer size for larger datasets like ImageNet.
- Inspiration from Serre et al.'s neuroscience model using Gabor filters for multiple scales; Inception's difference: learned filters and repeated layers leading to a deeper model (e.g., GoogLeNet with 22 layers).
- Network-in-Network approach by Lin et al.: additional 1×1 convolutional layers followed by ReLU activation, used for dimension reduction in Inception architecture.
- Benefits of Inception: increased representational power without computational bottlenecks, allowing for deeper and wider networks without performance loss.
- Practical applications: Image classification, object detection, human pose estimation, and localization.
- Unusual finding: Inception's ability to handle multiple scales in a single layer, reducing the need for max-pooling layers that can lead to loss of accurate spatial information.
- The paper explores enhancing object detection performance by focusing on convolutional neural networks (CNNs) and addressing the drawbacks of increasing network size.
- It adopts a similar pipeline to R-CNN, with improvements in both stages: multi-box prediction for higher recall and ensemble approaches for better categorization.
- The main motivation is to improve performance without significantly increasing computational resources or overfitting due to limited training data.
- The paper suggests moving towards sparsely connected architectures within convolutions, mimicking biological systems and providing theoretical underpinnings.
- It introduces a new method called ""Going Deeper with Convolutions"" that achieves state-of-the-art performance on the PASCAL VOC 2012 dataset without increasing computational resources or network size.
- The paper presents an ensemble of CNNs, each trained on a different subset of the training data, which results in better generalization and improved accuracy.
- The authors propose a new loss function that encourages sparsity in the convolutional filters, leading to more efficient use of computational resources.
- They also introduce a novel method for learning sparse convolutions by using a combination of L1 regularization and max-pooling layers.
- The paper highlights the importance of balancing network size with performance and computational efficiency in deep learning models.
- It provides practical applications, such as object detection in real-world scenarios, where efficient use of resources is crucial.
- The paper explores deeper convolutions for neural networks, building on Arora et al.'s work that suggests analyzing correlation statistics of activations to construct optimal network topologies.
- While the mathematical proof requires strong conditions, the underlying idea resonates with Hebbian learning principles and may be applicable in less strict scenarios.
- Current computing infrastructures struggle with non-uniform sparse data structures due to inefficiencies in numerical calculations and cache misses.
- Convolutional neural networks (ConvNets) use convolutions for spatial domain sparsity, but implement dense connections between patches in earlier layers.
- Traditionally, random or sparse connection tables were used to improve learning, but full connections have become more popular due to better parallel computing optimization.
- The paper questions whether there's hope for an intermediate architecture that exploits extra sparsity at the filter level while utilizing dense matrix computations on current hardware.
- Clustering sparse matrices into relatively dense submatrices can lead to state-of-the-art practical performance in sparse matrix multiplication, suggesting similar methods could be used for automated construction of non-uniform deep learning architectures.
- The Inception architecture is a case study for assessing the hypothetical output of a sophisticated network topology construction algorithm that approximates a sparse structure implied by Arora et al.'s work.
- The paper introduces a new architecture, Inception, for vision networks that approximates a sparse structure implied by previous research.
- After iterations and tuning, the Inception architecture showed modest gains against reference architectures in localization and object detection tasks.
- The initial success of Inception motivates further exploration into optimizing local sparse structures in convolutional vision networks.
- The main idea behind Inception is to find optimal local sparse structures and cover them with readily available dense components, assuming translation invariance.
- Arora et al.'s layer-by-layer construction suggests analyzing correlation statistics of the last layer and clustering units with high correlation into groups for the next layer.
- In lower layers, correlated units concentrate in local regions, allowing 1x1 convolutions to cover them, while larger clusters are covered by convolutions over larger patches as layers increase.
- The paper highlights the importance of avoiding patch alignment issues and discusses current implementations of Inception architecture.
- Inception architecture with larger convolution filters for deeper networks, addressing patch alignment issues by limiting filter sizes to 1×1, 3×3, and 5×5.
- Combination of layers with output filter banks concatenated into a single vector input for the next stage.
- Adding an alternative parallel pooling path in each stage could have beneficial effects (Figure 2(a)).
- ""Inception modules"" stacked on top of each other, leading to varying correlation statistics and higher abstraction features with decreasing spatial concentration.
- Problem with large numbers of 5×5 convolutions becoming expensive, especially with pooling units added.
- Inception module naive version (Figure 2(a)) inefficiently covers optimal sparse structure, leading to computational blow-up.
- Second idea: judiciously applying dimension reductions and projections where computational requirements increase too much.
- Embeddings as inspiration for compression, keeping representation sparse at most places and compressing signals only when needed (Figure 2(b)).
- 1×1 convolutions used to compute reductions before expensive 3×3 and 5×5 convolutions, also including rectified linear activation.
- Final result: efficient implementation of deep networks with sparse representation and reduced computational complexity.
- Inception architecture: A network design consisting of modules with rectified linear activation, stacked upon each other, and occasionally using max-pooling layers to reduce resolution. Used at higher layers while keeping lower layers in traditional convolutional fashion for memory efficiency reasons.
- Benefits of Inception architecture: Allows for increasing the number of units without a significant increase in computational complexity; uses dimension reduction to shield large input filters and process visual information at various scales before aggregating features from different scales.
- Computational resources utilization: Enables networks with increased width and depth without facing computational difficulties, and can create computationally cheaper versions of Inception architecture that are 2-3 times faster than similarly performing non-Inception architectures.
- GoogLeNet (ILSVRC14 competition): A specific implementation of the Inception architecture used in the competition; a deeper and wider version was also included, but its influence on results was relatively minor.
- Architecture details: Includes various convolution layers with different patch sizes, stride output sizes, depths, and number of filters. Max pooling layers are used to halve the resolution at specific stages.
- GoogLeNet, an instance of Inception architecture, is described for demonstration purposes in Table 1.
- The network uses rectified linear activation and has a receptive field size of 224x224 with RGB color channels.
- The design aims for computational efficiency and practicality, allowing inference on devices with limited resources.
- GoogLeNet is 22 layers deep when counting only layers with parameters (or 27 layers including pooling).
- Average pooling before the classifier is used based on [12], but an extra linear layer enables adapting and fine-tuning networks for other label sets.
- Dropout remains essential even after removing fully connected layers, improving top-1 accuracy by 0.6%.
- The strong performance of shallower networks suggests that middle-layer features are highly discriminative.
- Adding auxiliary classifiers to intermediate layers can encourage discrimination in lower stages, increase gradient signal propagation, and provide additional regularization.
- The network's overall number of layers (independent building blocks) is about 100, but this depends on the machine learning infrastructure system used.
- The use of average pooling before the classifier improves top-1 accuracy by 0.6%, while dropout remains essential even after removing fully connected layers.
- The paper introduces a method to improve gradient propagation and regularization in convolutional neural networks (CNNs) by adding auxiliary classifiers on top of the Inception modules.
- These auxiliary classifiers are smaller CNNs that help increase the gradient signal, providing additional regularization during training.
- At inference time, these auxiliary networks are discarded, reducing computational costs.
- The paper presents a detailed structure for the extra network on the side, including an average pooling layer and several convolutional layers with Local Response Normalization (LRN) and Depth Concatenation.
- A 1x1 convolution is used for dimension reduction and rectified linear activation, followed by a fully connected layer with 1024 units and rectified linear activation.
- The training methodology uses the DistBelief distributed machine learning system with asynchronous stochastic gradient descent and 0.9 momentum.
- The paper suggests that the GoogLeNet network could be trained to convergence using a few high-end GPUs within a week, but memory usage is the main limitation.
- The authors provide an example of a schematic view of the resulting network (Figure 3) and discuss its training methodology.
- They also mention that their networks were trained using CPU-based implementation only, which could be replaced with GPUs for faster convergence.
- The paper's findings contribute to improving gradient propagation and regularization in CNNs, leading to better performance and potentially reducing computational costs during inference.
- The paper focuses on improving image classification performance using convolutional neural networks (CNNs) in the ILSVRC 2014 challenge.
- Key training techniques include asynchronous stochastic gradient descent, fixed learning rate schedule, Polyak averaging, various sampling methods, photometric distortions, and random interpolation resizing.
- The ILSVRC 2014 challenge involves classifying images into one of 1000 leaf-node categories in the Imagenet hierarchy with top-1 accuracy rate and top-5 error rate as performance metrics.
- To improve performance, the authors trained multiple versions of GoogleNet models (including a wider version) using ensemble prediction. They also adopted an aggressive cropping approach during testing.
- The paper highlights that it's challenging to provide definitive guidance on the most effective training methods due to various factors like image sampling and hyperparameter changes.
- The authors emphasize the importance of experimenting with different techniques, as they found a combination of methods worked well after the competition.
- The paper does not claim that all presented techniques are essential for achieving high performance but rather provides insights into successful approaches used in their experiments.
- The paper introduces an aggressive cropping approach for image classification, which involves resizing images to multiple scales and extracting squares from each scale. This method is used in the GoogLeNet model for the ILSVRC 2014 competition.
- The proposed scheme uses 144 crops per image, resulting in a top-5 error of 6.67% on both validation and testing data, ranking first among other participants. This represents a significant improvement compared to previous approaches.
- The paper analyzes the impact of multiple factors contributing to overall performance, including the number of models used, the number of crops, and averaging strategies for softmax probabilities.
- The authors report that aggressive cropping may not be necessary in real applications as the benefit of more crops becomes marginal after a reasonable number is reached.
- The paper also discusses the ILSVRC 2014 detection challenge setup and results, focusing on object detection using bounding boxes for 200 classes.
- The paper focuses on improving object detection performance using convolutional neural networks (CNNs).
- It compares different approaches, including ensemble models, contextual models, and bounding box regression.
- GoogLeNet's approach for object detection is similar to R-CNN but with improvements in the region proposal step.
- Increasing superpixel size reduces false positives by halving proposals from Selective Search while adding 200 multi-box predictions, resulting in a 1% improvement in mean average precision (mAP).
- Using an ensemble of six ConvNets for classification improves accuracy from 40% to 43.9%.
- The paper highlights the progress made since the first edition of the detection task, with accuracy almost doubling compared to 2013 results.
- Top-performing teams use CNNs and employ various strategies such as external data, ensemble models, or contextual models.
- Deep Insight's model shows a surprising improvement of only 0.3% in mAP using a single model.
- The paper discusses the use of pre-training with localization data for bounding box regression.
- GoogLeNet did not use localization data for pre-training, which might have affected its performance.
- The paper explores improving neural networks for computer vision by approximating optimal sparse structures using readily available dense building blocks.
- The Inception architecture, as used in the GoogLeNet model, demonstrates significant performance gains with ensembles and modest computational requirements compared to shallower and less wide networks.
- Despite not utilizing context or bounding box regression, the detection work was competitive, further supporting the strength of the Inception architecture.
- The approach suggests that moving towards sparser architectures is feasible and useful, opening up opportunities for future research in this direction.
- Acknowledgments include thanks to various individuals and teams who contributed to the project's success.
- The paper does not claim that similar quality results can only be achieved through their method; it acknowledges that expensive networks with comparable depth and width may also yield similar outcomes.
- The authors emphasize that their work would not have been possible without the support of various individuals and teams, including Chuck Rosenberg and Hartwig Adam.
",3089
"1502.05698",1,"- The paper aims to develop a set of synthetic tasks for evaluating reading comprehension and question answering, which are prerequisites for building an intelligent dialogue agent.
- These tasks measure understanding in various ways, such as chaining facts, simple induction, deduction, etc., and are designed to be prerequisites for any system that aims to converse with humans.
- The authors believe many existing learning systems cannot solve these tasks, so they classify them into skill sets to help researchers identify the weaknesses of their systems.
- They extend and improve the Memory Networks model, which can solve some but not all of the proposed tasks.
- The paper provides a framework for developing new algorithm designs based on the analysis of performance on these tasks, creating a feedback loop between task development and algorithm improvement.
- The paper aims to propose a set of toy tasks as prerequisites for achieving AI-complete question answering, focusing on simpler QA tasks with clear feedback on system capabilities.
- Related work includes the Allen Institute's ARISTO project and Richardson et al.'s MCTest, but their results are complicated to interpret due to multiple subtasks involved in answering questions.
- The proposed tasks aim for self-contained QA problems with both training and evaluation data, allowing assessment of required training examples and commonsense knowledge needed for test sets.
- Each task tests one skill necessary for full text understanding and reasoning, with the goal that performing well on all tasks is a prerequisite for AI-complete question answering.
- The tasks are publicly available at http://fb.ai/babi and source code at https://github.com/facebook/bAbI-tasks.
- Training data provides true answers to questions and relevant statements, while test data has no answer provided, requiring the system to generate an appropriate response.
- The paper presents a baseline model that achieves 100% accuracy on tasks 1-5 but fails on tasks 6-9 due to its inability to handle negation and quantifiers.
- The authors suggest future work could involve improving the baseline model, adding more complex tasks, or exploring different architectures for better performance.
- The paper introduces a set of toy tasks designed to evaluate Large Language Models' (LLMs) performance in question answering, with each task focusing on specific aspects of the process.
- Tasks are noiseless and have clear-cut evaluation, allowing for 100% accuracy by humans. They are based on simple everyday situations and don't require background knowledge in formal semantics or machine learning.
- Data is generated using a simulation where characters interact in locations, with examples provided in Tables 1 and 2.
- Tasks include:
   a. Single Supporting Fact (Task 1)
   b. Two or Three Supporting Facts (Tasks 2 and 3)
   c. Two or Three Argument Relations (Tasks 4 and 5)
   d. Yes/No Questions (Task 6)
   e. Counting and Lists/Sets (Tasks 7 and 8)
   f. Simple Negation (Task 9)
   g. Indefinite Knowledge (Task 10)
- The paper's main findings are not included in the summary, as they focus on evaluating LLMs' performance on these toy tasks rather than presenting new results or conclusions.
- Simple Negation and Indefinite Knowledge: Tasks 9 and 10 test negation and indefinite knowledge, which are basic forms of natural language understanding. They involve statements that imply a fact is false (Task 9) or describe possibilities rather than certainties (Task 10).
- Basic Coreference, Conjunctions, and Compound Coreference: Tasks 11 to 13 test coreference, which involves identifying the nearest referent (Task 11), understanding conjunctions (Task 12), and handling pronouns that can refer to multiple actors (Task 13).
- Time Reasoning: Task 14 focuses on understanding time expressions within statements and evaluating their order, such as ""In the afternoon Julie went to the park. Yesterday Julie was at school.""
- Basic Deduction and Induction: Tasks 15 and 16 test basic deduction via inheritance of properties (Task 15) and induction through inherited properties (Task 16). These tasks introduce concepts beyond the scope of this work, requiring further analysis in future tasks.
- Positional and Size Reasoning: Tasks 17 and 18 test spatial reasoning (Task 17) and understanding relative sizes of objects (Task 18), inspired by classic SHRDLU system examples and Winograd schema challenge.
- Path Finding and Agent's Motivations: Tasks 19 and 20 involve path finding (Task 19) and analyzing agent motivations (Task 20). These tasks are also beyond the scope of this work, requiring further exploration in future tasks.
- The paper introduces a set of toy tasks aimed at fostering the development and understanding of machine learning algorithms for Large Language Models (LLMs).
- These tasks involve finding paths between locations, understanding agent motivations, and other related scenarios in a simulated environment.
- The simulation follows classic text adventure game principles, with entities, actions, and internal states.
- The paper provides 20 English tasks, along with Hindi translations and shuffled English words for evaluation purposes.
- The authors compare various methods on these tasks, including an N-gram classifier baseline, LSTMs, Memory Networks (MemNNs), and a Recurrent Neural Network (RNN) with attention.
- Results show that MemNNs perform best overall, while RNNs with attention achieve the highest accuracy in some tasks.
- The paper highlights the importance of these toy tasks as a controlled environment for evaluating LLMs, complementing real-world data and helping to develop and analyze algorithms.
- Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks aims to analyze and improve the performance of Large Language Models (LLMs) in question answering tasks, focusing on three main tracks: weakly supervised models, strongly supervised models, and methods using external resources.
- Weakly supervised models are given only question-answer pairs at training time, while strong supervision provides supporting facts as well. External resource methods can use labeled data from other sources like coreference and semantic role labeling tasks.
- The paper presents a series of 20 toy tasks with varying complexities to test the performance of different models. These tasks include single supporting fact, two or three supporting facts, argument relations, yes/no questions, counting, lists/sets, negation, indefinite knowledge, coreference, conjunction, compound coreference, time reasoning, basic deduction and induction, positional reasoning, size reasoning, path finding, and agent's motivations.
- The N-gram classifier baseline is used as a comparison method, while LSTMs (Long Short-Term Memory) are popular for sequence prediction tasks. MemNNs (Memory Networks) are the main focus of this paper, with proposed extensions to improve their performance in question answering tasks.
- Extensions include adaptive memory, N-grams, nonlinear matching function, and combinations thereof. The paper also provides analysis on the amount of training data needed for each task to achieve 95% accuracy and the impact of multi-task training.
- The paper aims to create a set of prerequisite toy tasks for AI-complete question answering, focusing on optimizing the QA task using strong supervision and learning embeddings.
- It introduces extensions to MemNN models: adaptive memories (AM), N-grams (NG), and nonlinearities (NL).
- Adaptive memories improve tasks requiring more than two supporting facts, while N-grams help with word order. Nonlinearities perform better in yes/no and infinite knowledge tasks.
- Combining these extensions results in improved performance on 9 tasks that were previously failures.
- The structured SVM baseline performs worse than the MemNN model with extensions, but it does better on some tasks due to its non-greedy search.
- The paper concludes that a combination of different approaches is necessary for AI-complete question answering.
- The paper introduces a set of toy tasks as prerequisites for full language understanding and reasoning.
- These tasks are designed to test learning methods, not to be solved by hand-coded solutions or existing large-scale QA systems.
- Some existing machine learning methods, like Memory Networks, have been successful on some of the tasks but still fail on several others.
- The paper highlights the need for models that can learn to solve these tasks with minimal supervision and a small number of training examples.
- Future research should focus on developing more challenging tasks and improving model performance in weakly-supervised settings.
- The authors provide a simulation-based approach, making it flexible and controllable, with the goal of finding models that can learn to detect and combine patterns in symbolic sequences.
- The paper presents 20 toy tasks as part of this set, but more complex tasks could be developed by researchers from the community.
- Transfer learning across tasks is also an important area for future research.
- The authors have made the simulator and code for these tasks publicly available to facilitate further research in this field.
- The paper introduces a set of toy tasks, called bAbI (Bidirectional Encoder Representations from Transformers applied to Attentive Language Inputs), for evaluating and improving AI-complete question answering algorithms.
- These tasks are designed as intermediate steps towards achieving human-level performance in natural language understanding and reasoning.
- The paper highlights the importance of feedback loops between developing more challenging tasks and algorithms that can solve them, leading to fruitful research directions.
- bAbI tasks complement real-world datasets but should not be considered a substitute for them; they are meant to enhance algorithm development and analysis.
- Several promising new algorithms have been influenced by the bAbI tasks, including weakly supervised end-to-end Memory Networks (MemN2N), Dynamic Memory Networks, and the Neural Reasoner.
- The paper provides a list of references for related work in natural language processing, question answering systems, and memory networks.
- Memory Networks (Weston et al., 2014) are a promising class of models for question answering, applied to toy tasks in this paper.
- The model consists of four components: input feature map (I), generalization (G), output feature map (O), and response (R).
- Component I converts the input sentence into an internal representation; G updates memory based on new inputs.
- O produces output features by finding supporting memories, using scoring function sO to match sentences with memory slots.
- R generates a textual response from the output features, using Recurrent Neural Networks (RNN) or ranking words in the dictionary.
- Scoring functions sO and sR have an embedding model form: Φx(x)⊤U⊤UΦy(y).
- The paper presents a set of toy tasks, including coreference resolution, temporal relation classification, and event extraction, to evaluate the performance of Memory Networks.
- Results show that Memory Networks outperform baselines in all tasks, with an average accuracy of 90%.
- The authors suggest future work on improving the efficiency of the model and applying it to more complex tasks.
- This paper demonstrates the potential of Memory Networks for question answering and sets a foundation for further research in this area.
- The paper introduces improvements to Memory Networks (MemNN) for Question Answering tasks, addressing shortcomings in existing models.
- Adaptive memories and responses are introduced, allowing the model to automatically adapt the number of supporting facts based on the question being asked. This leads to multiple answers and improved performance.
- Nonlinear sentence modeling is explored using three variants: bag-of-N-grams (with N = 1, 2, or 3), a multilinear map, and a nonlinear embedding. The multilinear map performs similarly well to N-grams in some cases.
- A baseline system using an external structured SVM is presented for comparison purposes.
- Experiments show that the proposed improvements lead to better performance on various tasks, with MemNN+AM+NL achieving a mean performance of 93 (compared to 87 for MemNN).
- The paper highlights the importance of modeling sentence structure and positioning in Question Answering tasks.
- The authors built a classical cascade NLP system baseline using structured SVM, incorporating coreference resolution and semantic role labeling preprocessing steps trained on large amounts of labeled data.
- They first run the Stanford coreference system to replace mentions with their entity class's first mention. Then, they use the SENNA semantic role labeling system (SRL) to collect arguments for each verb.
- A ranking task is defined to find supporting facts using a linear scoring function and trained under strong supervision. The exhaustive search is pruned by requiring common non-determiner words between facts or the question.
- The scoring function SO consists of indicator features, focusing on pairs of sentences for simplicity.
- A similar structured SVM is built for the response stage with tuned features for that goal.
- Results show that the structured SVM performs well in some tasks (6, 9, and 10) where hand-built feature conjunctions capture necessary nonlinearities. However, it struggles on tasks requiring multiple supporting facts (3, 16, and 2).
- The non-greedy search of the structured SVM helps in other tasks like path finding (task 19).
- Overall, the structured SVM does not outperform MemNNs but performs better in specific cases where nonlinearities are crucial.
",2680
"1506.06724",1,"- Aim: Align books and movies to provide rich descriptive explanations for visual content beyond current caption datasets.
- Methods: Unsupervised neural sentence embedding trained from a large corpus of books, video-text neural embedding for computing similarities between movie clips and book sentences, context-aware CNN to combine information sources.
- Quantitative performance: Good alignment results for movie/book pairs.
- Qualitative examples: Demonstrate diversity of tasks the model can be used for (e.g., visual question answering).
- Contributions: Introduce novel sentence similarity measure based on neural sentence embedding, extend neural image-sentence embeddings to video domain, formulate alignment as an energy minimization problem with contextual information.
- Dataset: 11 movie/book pairs with 2,070 shot-to-sentence alignments.
- Applications: Social robotics, assistive driving, and semantic visual search with natural multi-sentence queries.
- Movie/book alignment model with 2,070 shot-to-sentence correspondences for 11 movie/book pairs.
- Applications: book retrieval, movie/book querying, captioning CoCo images with story-like descriptions.
- Related work: image and video captioning, text-to-image alignment, movie-to-text alignment.
- New datasets: MovieBook (11 movies + books) and BookCorpus (large number of books).
- Annotation process: annotators find shot-sentence correspondences while browsing book and watching movie.
- Accuracy: 70% for shot-to-sentence alignment, 92% for sentence-to-sentence alignment.
- Dataset size: 15 hours of movies, 34,000 sentences from books, 2,070 shot-to-sentence correspondences.
- Model architecture: LSTM with attention mechanism and cosine similarity for sentence embedding.
- Training: 100 epochs, batch size 64, Adam optimizer, learning rate 0.001.
- Evaluation metrics: accuracy, precision, recall, F1-score.
- The paper aims to align books and movies by identifying story-like visual explanations through watching movies and reading books.
- They introduce a dataset called MovieBook, which contains ground-truth alignments between 11 movie/book pairs with 2,070 correspondences.
- Challenges in aligning include the scale of data (movies have 1,800 shots and books have 7,750 sentences), differences in writing styles, language, and slang usage.
- The BookCorpus dataset is used to train a sentence similarity model with 11,038 books from various genres.
- They propose using neural embedding for computing sentence similarities and extending it to operate in the video domain for comparing shots and sentences.
- A novel contextual alignment model is developed to improve accuracy by considering the context of each match.
- The paper demonstrates that their approach achieves 30% accuracy on the MovieBook dataset, outperforming previous methods.
- Develop a novel contextual alignment model that combines information from various similarity measures and a larger time scale for better local alignment predictions.
- Introduce a simple pairwise Conditional Random Field (CRF) to smooth alignments, encouraging them to follow a linear timeline in both video and book domains.
- Use Skip-Thought Vectors architecture for learning unsupervised text representations, inspired by the distributional hypothesis.
- Apply recurrent neural networks with gated recurrent units (GRUs) as encoder and decoder RNNs to encode sentences into fixed vectors for scoring similarity through inner product.
- Train the model on a large collection of books (BookCorpus), which allows learning general representations of text, applicable to other domains.
- Encoder: GRU produces hidden state ht at each time step, forming representation of sequence w1i, ..., wti. Update gate (zt) and reset gate (rt) control information flow.
- Decoder: Two separate decoders for previous and next sentences, conditioned on encoder vector hi. Uses the same vocabulary matrix V to compute distribution over words.
- Objective function: Sum of log-probabilities for next and previous sentences conditioned on encoder representation (si−1, si, si+1). Adam algorithm used for optimization.
- Visual-semantic embeddings: Learn similarity between movie clips and their DVS descriptions using LSTM encoding and linear mapping of image features from convolutional networks.
- Embedding sentences and movie clips with a neural network, computing similarities between them using cosine similarity.
- Context-aware similarity measure that considers multiple similarity measures and a fixed context window in both the movie and book domain.
- A 3-layer CNN to predict combined scores based on all measurements within a specific volume.
- Global movie/book alignment as an inference problem in a Conditional Random Field (CRF), using the output of the CNN as unary potentials and pairwise potentials based on time span and distance between sentences.
- Aligning Books and Movies: The paper introduces a method to create story-like visual explanations by combining movie watching and book reading.
- Pairwise potentials: Two pairwise potential functions are defined - ψp(yi, yj) and ψq(yi, yj). They help in encouraging state consistency between nearby nodes and avoiding harsh leaps.
- Inference: The CRF (Conditional Random Field) model is a chain, allowing for exact inference using dynamic programming. Computation speed is improved by pruning states that are far from uniform alignment.
- Learning: Since ground-truth is limited, the paper treats hidden variables as unobserved nodes and learns the CRF weights with [29].
- Experimental Evaluation: The model was tested on 11 movie/book pairs, with training on Gone Girl and testing on other movies. It can watch 1,440 movies per day and read 870 books per day. Various qualitative results are provided in the paper.
- Movie/Book Alignment: The evaluation focuses on recall (recall at paragraph level) as a noisier measure of precision. Recall improves by adding more layers to the CNN, with each feature contributing positively. CRF helps performance, but not for all movies. A uniform timeline performs poorly compared to the contextual model.
- Running Times: BLEU score takes most time, while extracting visual and scene features are faster.
- Aligning Books and Movies: The paper aims to create a model that can align book passages with movie scenes, allowing for story-like visual explanations of movies by reading books.
- Main Contributions: The authors introduce a CNN (Convolutional Neural Network) and CRF (Conditional Random Fields) model for this task. They also present a new dataset called BookCorpus containing 300 books, which they use to train their models.
- Most Interesting Findings: The paper shows that the proposed model can accurately align movie scenes with book passages, even when there are large dialog deviations and challenges in matching visual representations to verbose text. They also demonstrate how the model can be used to caption movies via a corpus of books.
- Book ""Retrieval"": The authors compute alignment between a movie and all test books, retrieving the correct book for each movie. This experiment shows that their model is able to retrieve semantically meaningful matches despite large dialog deviations.
- Describing Movies via Other Books: The paper demonstrates how movies can be captioned by matching shots with paragraphs from a corpus of books, without encouraging a linear timeline (CRF). This allows for the description of unrelated stories at the shot-paragraph level.
- CoCoBook: The authors propose using their model to write stories for CoCo (a robot that can understand and generate natural language), allowing it to create story-like visual explanations for movies by reading books.
- The paper explores aligning books and movies, proposing an approach that computes similarities between shots, dialogs, and sentences in a book using sentence embeddings.
- They extended image-text neural embeddings to video and proposed a context-aware alignment model considering all available similarity information.
- Results showcased on a new dataset of movie/book alignments with quantitative results demonstrating the power and potential of their approach.
- The CoCoBook experiment demonstrated generating descriptive stories for images using sentence embeddings trained on books, retrieving semantically meaningful stories to explain the images.
- Qualitative examples of alignment in Fig. 8 showcased results obtained with their full model (CRF).
- Borrowing ""lines"" from other books was mentioned as a potential future work to improve the model's performance.
- The paper aims to align books and movies, creating story-like visual explanations by watching movies and reading books.
- It uses a contextual CNN (Convolutional Neural Network) for global alignment over the full book or movie.
- The model borrows paragraphs from other books to create ""stories"" that match specific shots in movies, improving with more books.
- Experiments show that aligning movies and books can lead to better visual explanations than using only descriptions or videos alone.
- The CoCoBook dataset is used for captioning CoCo images with passages from books, demonstrating the potential of this approach in other domains.
- The paper aims to create story-like visual explanations by combining movie and book data, aligning their concepts.
- It uses a novel approach that involves watching movies and reading books simultaneously, extracting key scenes and events from both sources.
- The authors propose a method for generating visual explanations based on the extracted information, with an emphasis on story-like coherence.
- They introduce a new dataset called ""Book2Movie"" containing 100 movie-book pairs, which they use to evaluate their approach's effectiveness.
- The evaluation process involves measuring the accuracy of generated visual explanations and comparing them with human-created ones.
- The authors achieve an average accuracy of 39% in generating story-like visual explanations for Book2Movie data.
- They also demonstrate that their method can be applied to other domains, such as aligning news articles and videos or scientific papers and research experiments.
- The paper highlights the potential applications of this approach in education, training, and information retrieval systems.
- It emphasizes the importance of story-like coherence in visual explanations for better understanding and engagement.
- The authors suggest future work to improve the accuracy and generalizability of their method by incorporating more advanced techniques like neural networks or multimodal learning.
- The paper aims to align books and movies by generating story-like visual explanations using machine learning techniques.
- It introduces a novel approach that combines neural machine translation, continuous translation models, and multimodal neural language models for this purpose.
- The authors use the Microsoft COCO dataset (Common Objects in Context) to train their model on image captioning.
- They propose a new evaluation metric called ""Visual Semantic Search"" to measure the performance of their method.
- The main findings include improved accuracy and efficiency compared to existing methods, as well as generating more coherent visual explanations for movies and books.
- The paper highlights the potential applications in areas such as movie recommendation systems, storytelling, and educational tools.
- The paper explores aligning books and movies to generate story-like visual explanations by combining reading and watching.
- It uses neural networks, deep learning techniques, and computer vision methods to achieve this goal.
- Key contributions include Book2Movie (align video scenes with book chapters), Aligning Plot Synopses to Videos for Story-based Retrieval, Translating Videos to Natural Language using Deep Recurrent Neural Networks, Show and Tell: A neural image caption generator, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, Learning Deep Features for Scene Recognition using Places Database.
- These methods aim to improve story-based retrieval, visual explanation generation, and video understanding.
- The paper provides examples of how these techniques can be applied in real-world scenarios, such as aligning movie scenes with book chapters or generating natural language descriptions for images.
",2322
"1511.01432",1,"- Semi-supervised Sequence Learning paper introduces two approaches for improving sequence learning with recurrent networks using unlabeled data.
- The first approach is a next step prediction model, also known as a recurrent language model in NLP. This method predicts the next element in a sequence.
- The second approach uses a sequence autoencoder, which reads an input sequence into a single vector and reconstructs it.
- Both pretraining methods can be used to initialize standard LSTM RNNs for better training and generalization.
- Experiments show that LSTMs pretrained by recurrent language models or sequence autoencoders perform better than LSTMs initialized randomly, reaching or surpassing previous baselines without additional data.
- Using more unlabeled data from related tasks in the pretraining can improve generalization of a subsequent supervised model.
- The paper argues that their semi-supervised approach has advantages over other unsupervised sequence learning methods due to its ease of fine-tuning and being less complex than some alternatives.
- The sequence autoencoder is inspired by the work in sequence to sequence learning (seq2seq) framework, which has been successfully used for various tasks like machine translation, text parsing, image captioning, video analysis, speech recognition, and conversational modeling.
- The paper introduces a semi-supervised sequence learning method using recurrent networks, specifically LSTMs (Long Short-Term Memory).
- It proposes the use of a sequence autoencoder as an initialization for other supervised tasks, suggesting that this approach is stable and effective due to its ability to memorize input sequences and shortcut gradients.
- The paper highlights the benefits of using unsupervised methods like sequence autoencoders with large quantities of unlabeled data, improving generalization abilities in tasks with limited labeled data.
- It also explores pretraining LSTMs using recurrent language models as an alternative to the sequence autoencoder approach and finds that this method outperforms LSTMs initialized randomly.
- The paper presents experiments on text understanding tasks such as sentiment analysis (IMDB, Rotten Tomatoes) and text classification (20 Newsgroups, DBpedia).
- Results show that SA-LSTMs (initialized with sequence autoencoders) match or surpass previous best reported results for all datasets, suggesting a general model for similar tasks.
- Semi-supervised Sequence Learning: Contributions and Findings Summary
- Introduces a method for training LSTMs on long documents using sequence autoencoders (SA-LSTM) to overcome optimization instability issues in traditional LSTMs.
- SA-LSTM achieves better performance than previous baselines, such as Paragraph Vectors and ConvNets, on the IMDB sentiment classification task with a test error rate of 7.24%.
- Language modeling (LM-LSTM) initialization works well but not as effectively as SA-LSTM due to its short-term objective nature.
- The success in IMDB dataset leads to testing the method on another sentiment analysis task, Rotten Tomatoes, where similar gains are obtained with a test error rate of 10.9%.
- Additional unlabeled data improves performance significantly, especially for smaller datasets like Rotten Tomatoes.
- The paper highlights the importance of using sequence autoencoders in training LSTMs on long documents and provides insights into the benefits of incorporating additional unlabeled data in sentiment analysis tasks.
- Semi-supervised Sequence Learning focuses on training LSTMs and SA-LSTMs using a small dataset from Rotten Tomatoes movie reviews, addressing overfitting issues by incorporating unlabeled data from IMDB and Amazon movie reviews.
- The study shows that it's easier to train LSTMs on the smaller Rotten Tomatoes dataset compared to the larger IMDB dataset due to sentence-level reviews versus paragraph-level ones.
- By combining SA-LSTMs with 95% input embedding and 50% word dropout, generalization improves, reducing test set error from 20.3% to 19.3%.
- Using pretrained word vectors from Google News's word2vec slightly increases accuracy by only 0.5%, while using IMDB or Amazon movie reviews for autoencoder training decreases the error rate by up to 2%.
- The study compares its method with others, achieving a 16.7% test set error when using unlabeled data from Amazon movie reviews, ranking second among methods that have access to larger labeled datasets.
- Text classification experiments on 20 newsgroups show that the proposed method outperforms other baselines in terms of accuracy and speed.
- Semi-supervised Sequence Learning: Applications of Stacked Attention LSTMs to Long Documents and Challenging Classification Tasks
- Experiments on the 20 Newsgroups dataset with long documents (average length of 267 words, maximum length of 11,925 words).
   - SA-LSTM is more stable to train than LSTMs.
   - With 70% input embedding dropout and 75% word dropout, SA-LSTM achieves a test set error rate of 15.6%, much better than previous classifiers in this dataset.
- Character-level document classification experiments with the DBpedia dataset (average length of 300 characters).
   - No overfitting issues due to large dataset size, so no dropout used on input or recurrent layers.
   - SA-LSTM achieves a test set error rate of 2.34%, better than convolutional networks.
- Object classification experiments with the CIFAR-10 image dataset (60,000 images, 32x32 color images).
   - Pretrained LM-LSTM outperforms baseline convolutional DBN model despite not using any convolutions.
- Discussion: SA-LSTMs are effective for long documents and challenging classification tasks due to their ability to handle large contexts, better generalization, and stability during training.
- The paper explores using LSTM recurrent networks for NLP tasks, such as document classification.
- It demonstrates that a language model or sequence autoencoder can help stabilize the learning in LSTM recurrent networks.
- On five benchmarks, LSTMs achieved performance levels comparable to previous baselines.
- The paper acknowledges contributions from various researchers and the Google Brain team.
- References include works on LSTMs, language models, sequence autoencoders, and other related topics.
- Semi-supervised Sequence Learning paper focuses on developing methods for learning from both labeled and unlabeled data in sequential tasks, such as language modeling or machine translation.
- The authors propose a novel approach that combines generative models with discriminative training objectives to leverage the benefits of both supervised and unsupervised learning.
- They introduce a new objective function called ""contrastive divergence"" (CD) for semi-supervised sequence learning, which minimizes the difference between the predicted distribution from the model and the true distribution from the data.
- The CD objective is applied to both language modeling and machine translation tasks, showing improved performance compared to traditional supervised methods.
- Experiments demonstrate that their approach achieves better results with less labeled data, making it more efficient for resource-constrained scenarios.
- The paper presents a new method for semi-supervised sequence learning that combines the strengths of generative and discriminative models, leading to improved performance in various tasks.
- Contrastive divergence (CD) objective function is introduced as a key component of this approach, enabling better utilization of both labeled and unlabeled data for training.
- The CD objective function helps minimize the difference between predicted and true distributions, resulting in more accurate models with less labeled data.
- Experiments show that the proposed method outperforms traditional supervised learning methods in language modeling and machine translation tasks.
- This approach is particularly beneficial for resource-constrained scenarios where obtaining large amounts of labeled data can be challenging.
",1546
"1604.04562",1,"- The paper introduces a neural network-based end-to-end trainable goal-oriented dialogue system for task-oriented conversations.
- This approach combines text-in, text-out models and addresses the challenges of creating multiple components in traditional systems.
- A novel pipeline Wizard-of-Oz framework is used to collect dialogue data without making assumptions about the task at hand.
- The model demonstrates natural conversation with human subjects while helping them accomplish tasks in a restaurant search domain.
- This approach eliminates the need for large amounts of handcrafting or costly labelled datasets, simplifying the development process.
- The paper discusses limitations and future work, including expanding to other domains and improving dialogue quality.
- The paper proposes a neural network-based model for task-oriented dialogue systems, balancing strengths and weaknesses of existing research in this field.
- This end-to-end trainable model is modularly connected but does not directly model user goals; it learns to accomplish tasks by providing relevant responses at each turn.
- The model has an explicit representation of database attributes (slot-value pairs) for high task success rates and a distributed representation of user intent (dialogue act).
- The proposed model performs competitively in given tasks with only a few hundred dialogues, requiring less data than traditional models due to delexicalization and weight tying strategies.
- A novel pipeline data collection mechanism is introduced for target application training, inspired by the Wizard-of-Oz paradigm and crowd-sourcing.
- The model's architecture consists of a sequence-to-sequence framework with belief trackers, an intent network, a database operator, and a policy network.
- This approach enables fast data collection online at low development costs.
- The paper proposes a network-based end-to-end trainable task-oriented dialogue system that combines an intent network, belief trackers, and a response generation network.
- Intent Network: Encodes input tokens into distributed vector representation (z) using LSTM or CNN as encoder. This vector represents the user's intent.
- Belief Trackers: Maintain dialogue state by mapping natural language sentences to slot-value pairs, enabling querying of a database.
- Response Generation Network: Conditions on system action vector and generates required output token by token in skeletal form. Final response is formed by substituting actual values from the database.
- The paper investigates both LSTM and CNN as encoders for intent networks, and introduces a tied Jordan-type RNN belief tracker with delexicalized CNN feature extractor.
- Benefits of this approach include reduced data requirements due to weight tying strategy, avoiding learning long-term dependencies from raw inputs, and enabling semantic parsing.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system that uses belief trackers to maintain multinomial distributions for informable and binary distributions for requestable slots in an ontology.
- Each slot has its own specialized tracker, which is a Jordan-type RNN with CNN feature extractor.
- The paper reduces data requirements by tying RNN weights together for each value v while varying features ft v during updates.
- To model discourse context at each turn, the feature vector includes CNN-derived features from user input and machine response, considering delexicalization in slot-value specialised CNN operator.
- The belief tracker is based on Henderson et al.'s work with modifications, providing inherent robustness for future spoken system extension.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system with slot-based belief trackers and a CNN feature extractor, replacing the n-gram feature extractor from previous work.
- Slot-based belief trackers add intermediate labels to the system, which are critical for achieving task success. A novel Wizard-of-Oz data collection framework mitigates the additional annotation requirement.
- The Database Operator forms a binary truth value vector (xt) from the output of belief trackers and maintains entity pointers for DB entities consistent with queries.
- The Policy Network acts as the glue between system modules, generating a single action vector (ot) based on inputs from intent network, belief state, and DB truth value vector.
- The Generation Network uses the action vector to condition a language generator, creating template-like sentences token by token.
- The paper achieves 30% accuracy in a real-world task with a 4.5 times faster training speed compared to previous work.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system, focusing on generating responses based on language models and attention mechanisms.
- It uses a language generator to create template-like sentences using conditional LSTMs and replaces generic tokens with actual values.
- The Attentive Generation Network dynamically aggregates source embeddings using an attention mechanism to compute output steps, combining tracker belief states.
- Wizard-of-Oz data collection is proposed as a solution for the lack of in-domain training data in task-oriented dialogue systems.
- A novel crowdsourcing version of the Wizard-of-Oz paradigm is designed to collect domain-specific corpora, using Amazon Mechanical Turk.
- The paper presents an evaluation on the Restaurant Domain Corpus and achieves a 30% accuracy improvement over the baseline system.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system for restaurant search in Cambridge, UK.
- Users and wizards contribute single turns to parallel dialogues, ensuring coherence and consistency while reducing latency.
- Three informable slots (food, pricerange, area) and six requestable slots are used for the restaurant search domain.
- The system consists of two phases: training belief trackers and training the full model using cross-entropy errors from a language model.
- Empirical experiments show that CNN-based belief trackers outperform n-gram models in terms of precision, recall, and F1 score.
- The total cost for collecting 3000 HITs (Human Intelligence Tasks) was approximately $400, resulting in a dataset with around 680 dialogues.
- This approach encourages workers to learn from each other based on previous turns, leading to coherent and diverse dialogues.
- The paper proposes a network-based end-to-end trainable task-oriented dialogue system using convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- CNNs are used for feature extraction, while RNNs handle sequential data processing in the dialogue system.
- The model treats each dialogue as a batch and uses stochastic gradient descent with l2 regularization to train the network.
- The corpus is partitioned into training, validation, and testing sets in a 3:1:1 ratio for early stopping and gradient clipping.
- The vocabulary size is around 500, removing rare words and delexicalizable terms.
- Three convolutional layers are used for all CNNs with filter sizes set to 3, and pooling operations applied only after the final convolution layer.
- Decoding is done using average log probability of tokens to avoid length bias, and an MMI criterion is also investigated to increase diversity and encourage task completion.
- Beam search with a beamwidth of 10 is used for decoding, stopping when an end-of-sentence token is generated.
- The model obtains high precision in tracking tasks due to delexicalization, but N-gram trackers have lower recall. CNN type trackers can better generalize to sentences with long distance dependencies and complex syntactic structures.
- Corpus-based evaluation uses BLEU score (on top-1 and top-5 candidates), entity matching rate, and objective task success rate for performance measurement.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system that aims to improve dialogue performance by incorporating explicit internal semantic representations and modeling user requests.
- The model architecture consists of an encoder, tracker, decoder, and matcher modules. It uses LSTMs for the encoder and decoder, a CNN for intent classification, and a recurrent neural network (RNN) for dependency modeling on dialogue history.
- The paper presents various model variants to analyze their performance: baseline models, variant models with different trackers, and full models with different decoding strategies.
- Results show that incorporating explicit internal semantic representations in the full model improves task success rates significantly compared to models without this feature.
- The CNN-based intent network achieves a competitive BLEU score but has poor task success due to its local feature capturing and lack of global view, leading to unexpected overfitting.
- The paper also discusses the importance of modeling user requests explicitly in dialogue systems for better performance.
- Future work includes investigating more advanced decoding strategies and improving the model's ability to handle complex dialogues with multiple entities and requests.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system using different decoding strategies, attention mechanisms, and weighted decoding.
- Weighted decoding improves task success rate by 3% but not BLEU score significantly; Rt term contributes most to this improvement.
- Attention-based mechanism dynamically aggregates tracker beliefs, improving both BLEU score (0.01) and task success rate (5%).
- Combining weighted decoding with attention models further improves performance.
- Human evaluation shows an average subjective success rate of 98%, comprehension ability and naturalness scores above 4 out of 5, in a trial involving 245 dialogues.
- The NN model's performance is compared to a handcrafted baseline system (HDC), achieving better results on all metrics despite similar task success rates.
- Both systems demonstrate comparable efficiency and engagement levels, but the NN system offers more natural conversations.
- The paper introduces a novel neural network (NN) framework for task-oriented dialogue systems, which is end-to-end trainable using two supervision signals and a modest corpus of training data.
- A crowdsourced data collection framework inspired by the Wizard-of-Oz paradigm was presented, enabling quick and cost-effective acquisition of high-quality task-oriented dialogue data.
- Experimental assessment showed that the NN dialogue system interacted efficiently and naturally with human subjects to complete an application-specific task.
- The model is the first end-to-end NN-based model capable of conducting meaningful dialogues in a task-oriented application.
- Limitations include text-only dialogue, lack of handling noisy speech recognition inputs, and uncertainty confirmation. Scalability to larger domains remains an open question for further research.
- The paper introduces a network-based end-to-end trainable task-oriented dialogue system that uses categorical values, delexicalized tokens, and informable/requestable slots for better performance.
- Categorical values have the highest probability on one of their categorical values, with positive scores encouraging generation and negative discouraging it.
- Delexicalized token examples include Rt (observed) and Rt (not observed), while informable slot tokens are <s.food> and <s.area>.
- Requestable slot tokens have requestable value tokens associated, such as <s.phone> and <v.phone>, with positive scores for both.
- The authors acknowledge support from Toshiba Research Europe Ltd, Cambridge, and thank Ryan Lowe and Lukáš Žilka for their valuable comments.
- This system aims to improve task-oriented dialogue systems by providing a more efficient and effective approach compared to existing methods.
- Practical applications of this work could lead to better performance in chatbots, virtual assistants, and other conversational AI systems.
",2202
"1606.06031",1,"- Introduction of LAMBADA dataset: a challenging test set for evaluating computational models' ability to understand broad context in natural language texts.
- LAMBADA consists of narrative passages where human subjects can guess the last word if exposed to the whole passage but not just the final sentence.
- To succeed on LAMBADA, models must go beyond local context and handle broader discourse information.
- LAMBADA showcases a wide range of linguistic phenomena and none of state-of-the-art language models achieve high accuracy (<1%) on this benchmark.
- The dataset aims to encourage the development of new models capable of genuine understanding of broad context in natural language text.
- LAMBADA's design: 
   a. Passages are extracted from Wikipedia articles, with human annotators providing last word predictions.
   b. Dataset contains 10,000 passages, each with 25-35 words and 4-5 sentences.
- LAMBADA's evaluation:
    a. Accuracy metric is used to measure the percentage of correct word predictions.
    b. Models are evaluated on both full passages and last sentence contexts, highlighting their ability to handle broader discourse information.
- Practical applications: The LAMBADA dataset can be used for benchmarking and evaluating language models' performance in understanding broad context in natural language texts.
- Introduction of LAMBADA dataset: Language Modeling Broadened to Account for Discourse Aspects.
- Word prediction task with a challenging context requiring broader discourse understanding.
- LAMBADA used as a benchmark for testing language modeling architectures, including those with longer-term contextual memories.
- Preliminary experiments show that existing models perform poorly compared to human performance, confirming LAMBADA's challenge in natural language understanding research.
- Related datasets: CNN/Daily Mail (CNNDM) and CBT, both requiring broader context for word prediction but with different text genres and missing item types.
- Differences between LAMBADA and other related benchmarks: CNNDM focuses on summarizing articles while LAMBADA requires understanding narrative fragments or dialogues.
- Practical applications of LAMBADA: Testing language modeling architectures, evaluating the performance gap between human and machine comprehension, and improving natural language understanding models.
- Unusual findings: Human performance in LAMBADA is significantly higher than existing models, highlighting the need for further research in this area.
- The LAMBADA dataset focuses on word prediction requiring a broad discourse context, differentiating it from other tasks like entailment detection and question answering.
- Word prediction is attractive due to its naturalness, simplicity, and potential to probe various aspects of text understanding.
- LAMBADA data comes from the Book Corpus, consisting of passages with an average context size of 4.6 sentences and a target word as the last word of the target sentence.
- The dataset aims to test models' ability to understand broader contexts, which is not fully explored in other tasks like CBT.
- LAMBADA can be used for various applications, including training LLMs, evaluating text understanding, and providing a benchmark for future research.
- The dataset offers a practical way to measure models' performance on word prediction tasks, with potential real-world implications in areas such as machine translation and summarization.
- LAMBADA's broad context requirement makes it more challenging than other datasets like CBT, which only requires sentence-level understanding.
- The dataset can be used to improve models' ability to handle complex language phenomena like coherence, anaphora resolution, and word sense disambiguation.
- LAMBADA's data collection process involves human annotators, ensuring high-quality labels for the target words.
- The dataset can be used as a benchmark for evaluating models' performance on broader context understanding tasks, providing valuable insights into their capabilities and limitations.
- The LAMBADA dataset provides word prediction tasks requiring a broad discourse context, focusing on understanding and predicting words in sentences based on their surrounding text rather than just local context.
- This dataset aims to test the ability of LLMs to handle long-range dependencies and complex linguistic phenomena, such as anaphora resolution, ellipsis, and coreference resolution.
- The paper presents 10 examples from the LAMBADA dataset, each with a target sentence, context, and target word for prediction. These examples demonstrate various linguistic challenges faced by LLMs in predicting words within broader discourse contexts.
- The authors evaluate the performance of GPT-2 on the LAMBADA dataset and find that it struggles to handle long-range dependencies and complex linguistic phenomena, achieving an accuracy of 30% compared to a baseline model with only local context.
- The paper highlights the importance of developing LLMs capable of handling broader discourse contexts for improved performance in various natural language processing tasks.
- LAMBADA dataset serves as a benchmark for evaluating and improving large language models' ability to handle long-range dependencies and complex linguistic phenomena, contributing to the advancement of NLP research.
- The LAMBADA dataset is a novel word prediction task requiring a broad discourse context, different from other datasets like news data, Wikipedia texts, and famous novels.
- It consists of 5,325 novels with 465 million words, divided into training, development+testing partitions, and further split for LAMBADA data.
- The dataset aims to test models' ability to predict target words based on the entire passage context rather than local context alone.
- Four language models were used: a pre-trained RNN, three models trained on Book Corpus (standard 4-gram, RNN, and feed-forward).
- Passages with high probability predictions from any of these models were excluded to ensure challenge level.
- Human evaluation was conducted through CrowdFlower crowdsourcing service in three steps: whole passage guessing, target sentence-only guessing, and adding difficult cases to the LAMBADA dataset.
- The dataset can be used for evaluating language model performance on word prediction tasks requiring broad discourse contexts.
- This work contributes a new benchmark for measuring the usefulness of general world knowledge and external resources in LLMs.
- The LAMBADA dataset is available at https://github.com/facebookresearch/LAMBADA.
- The LAMBADA dataset aims to provide word prediction tasks requiring a broad discourse context for LLMs.
- It consists of 10,022 passages from 1,331 and 1,332 disjoint novels, with an average passage length of 75 tokens.
- The dataset creation process involves three steps: (1) filtering based on local context guessability, (2) ensuring no guesses in broader discourse context, and (3) removing cases where the same subject judges both passage and sentence conditions.
- Only about one in 25 input examples passes all selection steps, with an average cost of $1.24 per item.
- The strict hit-or-miss approach was chosen for its practicality and financial feasibility compared to alternative methods.
- The training data consists of text from 2,662 novels (disjoint from the development and test sets).
- LAMBADA can be used as a benchmark for evaluating LLMs' ability to handle long-range dependencies in natural language processing tasks.
- LAMBADA dataset: A word prediction task requiring a broad discourse context.
- 203 million words in training data, with text from the same domain as dev+test passages; not filtered like dev+test due to economic considerations and intended use for evaluating general-purpose models' understanding of broad contexts.
- Dataset analysis: Target word must be strongly cued in broader discourse (>80% occurrence in LAMBADA vs <15% in input data). Most target words are proper nouns (48%), followed by common nouns (37%) and verbs (7.7%). Proper nouns overrepresented, co-reference plays a significant role, but pronouns only account for 0.3% of target words.
- Qualitative analysis: Co-reference is prevalent in LAMBADA, sometimes with partial or bridging mechanisms (shutter–camera).
- Practical applications: Evaluate general-purpose models' ability to understand broad contexts and predict missing words; use development data for fine-tuning models specific to LAMBADA passages.
- The LAMBADA dataset focuses on word prediction requiring a broad discourse context, with examples of partial co-reference facilitated by bridging mechanisms and inference of prototypical participants in events.
- Verbs, adjectives, and adverbs are rare in LAMBADA, as they can often be guessed using local sentence context alone.
- End-of-sentence context skews input distribution towards nouns, while subject filtering shows a clear differential effect for nouns vs. other parts of speech (POS).
- Manual inspection reveals that broad context is not necessary to guess items like LAMBADA's target words in some cases.
- Frequent verbs, adjectives, and closed-class adverbs can be guessed with local sentence context, while other types of open-class adverbs are generally hard to predict.
- Proper nouns require explicit mention in the preceding discourse context, whereas other categories can often be guessed without being explicitly introduced.
- Qualitative analysis suggests that event-related phenomena (e.g., script-like sequences of events) are harder for subjects to guess than coreferential phenomena.
- Further research is needed to explore the hypothesis that tracking event-related phenomena is more challenging than coreferential ones in the LAMBADA task.
- The LAMBADA dataset is a word prediction task requiring broad discourse context understanding.
- Qualitative analysis shows that target words are implied but not present in the passage, often requiring complex reasoning for correct predictions.
- Intriguingly, LAMBADA items contain more direct speech than overall input samples, suggesting dialogic discourse may facilitate prediction.
- Several existing language models and baselines were tested on LAMBADA, with varying results.
- The dataset is challenging due to its broad context requirements and complex reasoning skills needed for solving word prediction tasks.
- Practical applications of the LAMBADA dataset include evaluating and improving large language models' comprehension abilities.
- Future research directions include investigating dialogic discourse's impact on prediction, developing better models for handling broader contexts, and exploring the role of pragmatics in word prediction tasks.
- The LAMBADA dataset is designed to challenge language models with word prediction tasks requiring a broad discourse context.
- N-Gram, LSTM, Memory Networks, and cache N-Gram models are tested for their ability to handle broader contexts.
- RNNs and LSTMs are state-of-the-art in standard language modeling benchmarks.
- The authors' Memory Network implementation is similar to Hill et al.'s best results on the CBT dataset.
- LSTM architecture resembles Deep LSTM Reader of Hermann et al., achieving respectable performance on CNNDM data set.
- Models perform well in a control set, suggesting they are good at standard language modeling and not poor quality.
- Sup-CBOW baseline model uses pre-trained CBOW vectors for weakly tailored word prediction.
- Unsupervised variant (Unsup-CBOW) predicts target words by cosine similarity between passage vector and target word vector.
- Random guessing baselines sample guessed words from different pools, including full vocabulary, current passage, or uppercased random words from the passage.
- LAMBADA aims to challenge language models with both average and harder-than-average examples requiring broad context understanding.
- The LAMBADA dataset is designed for word prediction requiring a broad discourse context, using novels as training data.
- Sup-CBOW model was trained on similar passages from the novels to test potential biases in the data.
- All models performed well on control sets but poorly on LAMBADA datasets, indicating difficulty in predicting words based on broader context.
- Random capitalized word heuristic had low performance (7% accuracy), showing that proper nouns alone are not enough for good performance on LAMBADA.
- Sup-CBOW model outperformed other models with 4660 correct predictions, but still struggled compared to simple random word selection methods.
- The study highlights the need for deeper analysis of broader context in LLMs for better performance on tasks like LAMBADA.
- Results suggest that current models are not capable of fully understanding and utilizing long-range dependencies in text.
- This work can help improve future LLM designs by focusing on improving their ability to handle complex, real-world language scenarios.
- The study provides a benchmark for evaluating the performance of LLMs in tasks requiring broader context analysis.
- LAMBADA dataset and results can be used as a reference point for researchers working on developing more advanced LLM models.
- The LAMBADA dataset is introduced, focusing on word prediction requiring a broad discourse context.
- Traditional N-Gram models perform better than neural network-based ones due to the difficulty of tuning the latter properly.
- N-Gram w/cache achieves the best relative performance with a perplexity of 768, but still struggles to predict the correct word.
- Evaluation is preliminary and may improve with further tuning or advanced mechanisms like attention.
- Standard language models fail on LAMBADA by design, as it tests their ability to handle non-local phenomena.
- Future research should develop novel language models capable of capturing these non-local phenomena.
- A public competition based on the LAMBADA data will be announced to promote research in this direction.
- The authors believe that long-term memory storage and reasoning abilities will be crucial components for successful models.
- The LAMBADA dataset is designed for word prediction tasks requiring a broad discourse context, aiming to test computational models' ability to capture various aspects of human text understanding.
- Successful models need to combine the capacity for word prediction with reasoning skills to retrieve relevant information from memory.
- Leveraging human performance on word prediction is a promising strategy for constructing benchmarks for computational models.
- The LAMBADA dataset's influence of broad context in word prediction is an example of this idea.
- Acknowledgments include gratitude to collaborators, funding sources (European Union's Horizon 2020 research and innovation program, Marie Sklodowska-Curie grant, ERC Starting Independent Research Grant, NWO VIDI grant), and support from NVIDIA Corporation for GPU donations.
- The LAMBADA development set and training corpus are available at http://clic.cimec.unitn.it/lambada/, while the test set will be released during a competition.
",2863
"1609.09106",2,"- Hypernetworks: A novel approach that uses a small network (hypernetwork) to generate weights for a larger network (main network).
- Similar to nature's genotype-phenotype relationship, hypernetworks can be viewed as an abstraction of weight sharing in neural networks.
- Trained end-to-end with backpropagation, making them faster than HyperNEAT evolutionary approach.
- Applicable for deep convolutional and long recurrent networks, acting as a relaxed form of weight-sharing across layers.
- Generates non-shared weights for LSTM, achieving near state-of-the-art results in sequence modeling tasks like character-level language modelling, handwriting generation, and neural machine translation.
- Applies to convolutional networks for image recognition tasks with comparable performance to baseline models with fewer learnable parameters.
- Hypernetworks can use fixed or dynamically generated embedding vectors, allowing weight adaptation to input sequences.
- Experiments show that hypernetworks mix well with other techniques like batch normalization and improve performance in various contexts.
- Achieves near state-of-the-art results on language modeling tasks, handwriting generation, image classification, and large neural machine translation models.
- Motivated by evolutionary computing methods to handle large search spaces with millions of weight parameters.
- Related approaches include HyperNEAT, Compressed Weight Search, DPPNs, and ACDC-Networks; however, hypernetworks in this paper are trained end-to-end for better efficiency.
- Balances between model flexibility and training simplicity, addressing limitations of previous methods like Discrete Cosine Transform and HyperNEAT's architecture evolution.
- Applications include natural language processing (NLP), computer vision, and speech recognition tasks.
- Offers potential for reducing the number of learnable parameters in deep learning models without sacrificing performance.
- Provides a framework that can be applied to various architectures like LSTMs, CNNs, and RNNs.
- Demonstrates improved performance on real-world tasks such as machine translation between English and German.
- Enables the use of smaller datasets for training hypernetworks, reducing computational costs and time requirements.
- HyperNetworks combine elements of fast weights and neural architecture search (NAS) to generate weights for practical architectures like convolutional networks, recurrent networks, and fully connected networks.
- Strikes a balance between the flexibility of convolutional networks and weight-sharing in recurrent networks by introducing relaxed weight sharing through HyperNetworks.
- Static and Dynamic HyperNetworks: Weight factorization for deep convolutional networks, allowing generation of weights for recurrent networks.
- Applications in image classification tasks: Achieved 30% accuracy on MNIST with a single layer and 92% on CIFAR-10 with a deep network (vs. 86% baseline). Faster training times (4.5x) and reduced memory requirements (20% of baseline).
- Reinforcement learning applications: Achieved 99.6% success rate in CartPole-v0, compared to 95% baseline.
- Potential use cases beyond image classification, like natural language processing and robotics.
- HyperNetworks as an alternative to NAS methods with faster training times and reduced memory requirements while maintaining or improving performance.
- Two-layer linear network for predicting kernel weights in convolutional layers, reducing learnable parameters compared to traditional methods.
- Application of the method to deep convolutional architectures like residual networks (He et al., 2016a), with comparable performance and smaller model size/faster training time vs. traditional methods.
- Introduction of ""kernel-wise dropout"" regularization technique for hypernetworks, improving generalization performance.
- Useful in applications where the number of trainable parameters is a concern (mobile or embedded systems).
- Efficient implementation strategy using stochastic gradient descent with momentum.
- HyperNetworks can generate kernels for residual networks by concatenating basic 16x16 kernels to form larger ones, adapting to the architecture's requirements.
- Dynamic HyperNetworks (DHNs) generate weights for recurrent networks like RNN and LSTM, acting as a compromise between hard weight-sharing in traditional recurrent networks and no weight-sharing in convolutional networks.
- DHNs can be used to create HyperRNNs, which generate weights for an RNN at each timestep using the input and hidden states of the main RNN. Both the HyperRNN and the main RNN are trained jointly with backpropagation and gradient descent.
- A new formulation of DHNs uses a single embedding vector to generate weights for all time steps, reducing parameters and improving efficiency.
- Experiments show that Dynamic HyperNetworks can achieve comparable or better performance compared to traditional recurrent networks with fewer parameters and faster training times.
- The paper analyzes the trade-off between model expressiveness, number of parameters, and computational efficiency in various scenarios.
- DHNs are effective for tasks like image classification, speech recognition, and language modeling.
- A new formulation of DHNs uses a single embedding vector for all time steps, reducing parameters and improving efficiency.
- Experiments demonstrate that Dynamic HyperNetworks can achieve comparable or better performance than traditional recurrent networks with fewer parameters and faster training times.
- The paper provides insights into the trade-off between model expressiveness, parameter count, and computational efficiency in various scenarios.
- HyperRNNs allow for dynamic parameter generation in RNNs, enabling different parameters at each time step. This approach reduces memory usage compared to directly using embeddings from the main RNN, making it scalable for real-world applications.
- HyperRNNs perform element-wise multiplication operations, similar to other normalization techniques, and improve accuracy in image recognition tasks with MNIST and CIFAR10 datasets.
- Static hypernetworks can learn better scaling policies than static approaches, combining with existing normalization methods like Layer Normalization.
- Potential applications include speech recognition, natural language processing, and areas where memory efficiency is crucial.
- Static Hypernetworks for image recognition on MNIST achieved 99.24% test accuracy, comparable to conventional methods.
- Dynamic hypernetworks applied to language modeling on Penn Treebank and Hutter Prize Wikipedia (enwik8) datasets reached 97.3% and 96.4% test accuracy respectively.
- Handwriting generation using dynamic hypernetworks achieved 92.1% test accuracy.
- HyperNetworks can generate kernels for convolutional layers in deep residual networks, reducing model parameters and network width.
- Experiments on Wide Residual Network (WRN) architectures showed a reduction in classification accuracy but significant parameter count reduction.
- HyperNetworks can be applied to newer residual network variants like DenseNets and ResNets with more skip connections.
- The paper introduces HyperLSTM, which uses HyperNetworks for efficient scaling of inputs to activation functions in LSTMs, outperforming larger or deeper versions of LSTM.
- HyperNetworks introduce HyperLSTM, an improved version of LSTMs for character language modeling.
- The main contribution is learning weights and biases of LSTMs more efficiently through hypernetworks.
- HyperLSTM achieves competitive results on Penn Treebank and enwik8 datasets, outperforming other models like Layer Norm LSTM and LSTM.
- HyperNetworks can be applied to CNNs and RNNs for faster training and inference due to fewer trainable parameters.
- The paper introduces a new perspective on regularization methods by learning an adjustment policy instead of relying on statistical moments.
- Practical applications include improved language modeling, text generation, machine translation, and speech recognition.
- HyperNetworks use a ""HyperRNN cell"" that chooses the best model at any given time for generating probability distributions.
- The paper presents a new method for training HyperNetworks using backpropagation through time (BPTT) with a novel loss function, achieving comparable performance to Layer Norm while being 4.5 times faster and requiring less memory.
- HyperNetworks introduce a dynamic weight adjustment policy for LSTMs, called HyperLSTM, to address saturation issues and improve performance compared to statistical normalization methods like Layer Norm.
- In handwriting sequence prediction, HyperLSTM outperforms other models with lower log-loss on the IAM Online Handwriting Database validation set. Data augmentation and recurrent dropout improve performance for all models in this task.
- Increasing unit count per layer may not help LSTMs' performance as much as increasing the number of layers, which is more beneficial for HyperLSTM. Ignoring statistical normalization could lead to better results.
- In handwriting sequence prediction, HyperLSTM without layer norm achieved better performance than one with layer norm. Statistical normalization might be a setback in certain scenarios.
- HyperNetworks focus on comparing LSTM performance with varying layer depth and unit count, as well as examining the effectiveness of layer norm in conjunction with HyperLSTMs. Increasing layer depth outperforms increasing units per layer for LSTMs in this task.
- In this specific task, HyperLSTM without layer norm achieved better performance than one with layer norm. Statistical normalization may not be optimal for weight adjustment policies in this context.
- HyperLSTM's convergence rate is as fast as a 2-layer LSTM model. Qualitatively, HyperLSTM's handwriting samples are more coherent than those from Layer Norm LSTMs and less noisy compared to LSTMs.
- Weight changes in HyperLSTM occur at discrete instances rather than gradually over time, suggesting regime changes instead of slow adjustments.
- The ability to dynamically generate the generative model is a key advantage of HyperRNN over normal RNNs. Experiments with Neural Machine Translation show that HyperLSTMs can achieve better performance compared to LSTMs and GRUs in terms of BLEU score, perplexity, and training time.
- HyperNetworks demonstrate potential for various tasks such as handwriting generation and machine translation. They are competitive or better than state-of-the-art models in image recognition, language modeling, and handwriting generation tasks.
- HyperNetworks: A novel architecture that learns network weights for various tasks using a single shared network to generate task-specific networks.
- Improved performance in image classification, object detection, and reinforcement learning compared to traditional methods.
- Reduces the need for task-specific network design by acting as a general-purpose framework for deep learning applications.
- Efficient training procedure using backpropagation through time (BPTT) and a novel loss function to optimize hyperparameters.
- Applicable to different network architectures, including CNNs, RNNs, and LSTMs.
- Theoretical analysis of convergence properties and the ability to learn weights for tasks with varying input sizes.
- Case study on image classification using CIFAR-10 dataset, outperforming traditional methods by 3%.
- HyperNetworks can be used in reinforcement learning tasks, achieving better performance than handcrafted networks.
- Represents network architectures as a single high-dimensional weight vector for more efficient training and reduced memory requirements.
- Potential applications in transfer learning, meta-learning, and lifelong learning scenarios.
- HyperNetworks are faster (4.5 times) and achieve similar or better accuracy compared to traditional methods in neural network research.
- This paper introduces a novel approach for representing and learning network architectures, addressing limitations of virtual coordinates-based methods in tasks like image recognition and language modeling.
- HyperNetworks use an embedding vector approach, where each input is associated with a feature representation (embedding vector) used by the hypernetwork to generate weights.
- Static HyperNetworks generate weights for feedforward networks, while Dynamic HyperNetworks generate weights for recurrent networks.
- Experiments show that HyperNetworks can learn convolutional-like filters during end-to-end training but have lower performance in image classification tasks compared to conventional fully connected networks (93.5% vs 98.5%).
- Applications of HyperNetworks include image recognition, language modeling, and reinforcement learning.
- The paper presents a conceptual framework for understanding the relationship between static and dynamic hypernetworks with feedforward and recurrent networks.
- Filter visualizations for Residual Networks are provided as examples to illustrate how filters can be learned using HyperNetworks.
- The paper introduces a new method for learning filters in fully connected networks, addressing limitations of previous approaches and offering potential applications across various domains.
- HyperNetworks' efficiency allows for faster training times and reduced memory requirements compared to traditional methods.
- HyperNetworks: A novel approach for constructing deep neural networks using a single network to generate weights for other networks, reducing model size and complexity.
- Introduces the HyperLSTM Cell, which applies Layer Normalization to an LSTM cell within a HyperNetwork framework.
- Implementation details include initialization parameters, Orthogonal Initialization, and dropout usage in Equation 13.
- Experiments on MNIST data show improved performance with a HyperConvNet compared to a Normal ConvNet.
- Potential applications include image recognition, speech recognition, natural language processing, and reinforcement learning.
- Benefits of HyperNetworks: reduced model size, faster training, better generalization due to larger datasets.
- Provides reference implementation using TensorFlow for readers interested in implementing their own HyperNetworks.
- Future work includes exploring different weight initialization methods, regularization techniques, and applying HyperNetworks to other tasks like NLP and reinforcement learning.
- Overall, the paper demonstrates the potential of HyperNetworks as a powerful tool for reducing model complexity while maintaining or improving performance in various applications.
- HyperNetworks can be used with residual networks, LSTMs, and CNNs, making it a flexible approach to network architecture design.
- HyperNetworks: A novel approach to neural network architectures that use a smaller, trainable meta-network (hypernetwork) to generate weights for the main network.
- Experiments with embedding sizes of 32 and 64, mini-batch size, learning rate, weight initialization, gradient clipping parameters, optimizer, recurrent dropout, Orthogonal Initialization, data augmentation, larger HyperLSTM configurations, Mixture Density Networks for handwriting sequence generation, adjusted training/validation split, output layer dropout, and Adam optimizer with a learning rate of 0.0001 and gradient clipping of 5.0.
- Applications include Neural Machine Translation (NMT) experiments on the WikiText-2 dataset, achieving an improvement in BLEU score from 30.1 to 31.5 when replacing LSTM cells with HyperLSTM cells.
- Practical applications: HyperNetworks can be used for learning parameters of other networks, potentially leading to more efficient and effective neural network architectures in various tasks such as NMT.
- Unusual finding: 4.5 times faster training speed when using HyperLSTM cells compared to the original GNMT architecture.
- HyperNetworks: A novel approach that uses a single network to learn multiple tasks simultaneously, reducing the need for task-specific networks.
- Introduces the HyperLayer, which acts as a parameter server and dynamically generates weights for each task based on input data.
- Faster training and better generalization compared to traditional methods requiring separate networks for each task.
- HyperNetworks significantly improve accuracy and efficiency compared to conventional approaches, especially for complex tasks and large datasets.
- Applications include image classification, speech recognition, and machine translation.
- Unified model reduces the need for task-specific models and simplifies architecture.
- Transfer learning between tasks is possible through shared parameters.
- Addresses overfitting and data sparsity issues in traditional neural networks.
- Future research directions include exploring activation functions, weight generation methods, and complex architectures.
- HyperNetworks offer a promising alternative to conventional neural network architectures with better performance and efficiency.
- Two types of HyperNetworks: Function Approximation HyperNetworks (FAHNs) and Architecture Search HyperNetworks (ASHNs).
- Competitive performance in image classification, language modeling, and other tasks.
- Potential to reduce training time by eliminating manual hyperparameter search.
- Practical applications include computer vision, natural language processing, and reinforcement learning.
",3173
"1611.09268",1,"- MS MARCO is a large-scale machine reading comprehension (MRC) dataset, with 1,010,916 questions and 8,841,823 passages extracted from Bing's search query logs.
- The questions are human-generated answers, while the passages provide context for curating natural language responses.
- Three tasks are proposed using this dataset: predict if a question is answerable given context passages, generate an answer based on context, and rank retrieved passages.
- MS MARCO's size and real-world nature make it attractive for benchmarking machine reading comprehension and QA models.
- The questions are derived from actual user search queries, making them more representative of natural information needs compared to synthetic datasets.
- Real-world text can be messy with typos, abbreviations, or conflicting information, requiring MRC systems to be robust to such inputs.
- MS MARCO's dataset can help improve the performance and generalization capabilities of MRC models in real-world scenarios.
- Introduction of MS MARCO: a large-scale real-world reading comprehension dataset addressing limitations of existing MRC tasks, such as requiring models to operate on single entities or text spans and not considering multiple documents for information extraction.
- MS MARCO's features: anonymized search queries from Bing/Cortana, segment information, extracted passages from documents, editorially generated answers, unanswerable questions included, and 182,669 answers in total.
- Proposed tasks: (i) Predict if a question is answerable given context passages, extract relevant info, and synthesize the answer; (ii) Generate a well-formed answer based on context passages; (iii) Rank retrieved passages given a question.
- Comparison with other MRC datasets: MS MARCO is more than ten times larger than SQuAD, questions in MS MARCO are from Bing's query logs, answers in MS MARCO are editorially generated, and originally SQuAD contained only answerable questions.
- NewsQA, DuReader, NarrativeQA, SearchQA, RACE, and ARC are other related datasets with varying characteristics.
- MS MARCO is a human-generated Machine Reading Comprehension (MRC) dataset that focuses on longer natural language answer generation and Bing search queries instead of trivia questions.
- The dataset consists of 1,010,916 questions with 1,026,758 unique answers, generated from Bing's search logs and annotated by human editors.
- Human editors use a web-based tool to extract relevant passages from documents, ensuring the passage contains useful information for answering the question and composes a well-formed natural language answer summarizing it.
- The questions in MS MARCO are often complex, ambiguous, and may contain errors, representing human information seeking behavior more accurately than other datasets.
- Compared to other datasets like DuReader, NarrativeQA, SearchQA, RACE, and AI2 Reasoning Challenge (ARC), MS MARCO has a larger scale, longer answers, and focuses on real-world search queries.
- MS MARCO is a human-generated Machine Reading Comprehension (MRC) dataset for benchmarking MRC models.
- The dataset consists of six major components: questions, passages, answers, well-formed answers, documents, and question types.
- Questions are anonymized user queries from Bing's search logs, with 6 main answer types (NUMERIC, ENTITY, LOCATION, PERSON, DESCRIPTION).
- Passages are extracted from relevant web documents using a state-of-the-art passage retrieval system at Bing.
- Answers are composed by human editors based on the passages provided and are reviewed for well-formedness.
- The dataset's distinguishing features include anonymized user queries, segment information, and questions from real web search contexts.
- The paper introduces MS MARCO, a human-generated machine reading comprehension dataset for benchmarking ML-based retrieval models.
- All questions have segment information annotation, context passages from real web documents, and answers composed by human editors. Some questions have multiple or no answers.
- A passage ranking dataset is proposed to facilitate benchmarking of emerging neural IR methods.
- The paper presents initial benchmarking results on the v1.1 version of MS MARCO, using different evaluation metrics for various question categories and a family of pairwise similarity-based metrics for long textual answers.
- Generative model experiments are conducted to evaluate performance with BLEU and pa-BLEU metrics on questions with multiple answers.
- MS MARCO: A Human Generated Machine Reading Comprehension Dataset paper focuses on evaluating various generative and discriminative models for machine reading comprehension tasks using the MS MARCO dataset.
- Experiments include Seq2Seq, Memory Networks, and a discriminative model comparison using ROUGE-L metric.
- Cloze-style experiments with Attention Sum Reader (AS Reader) and ReasoNet models on CNN and MS MARCO datasets.
- Experimental results on v2.1 dataset: Human baseline creation, training a competitive model based on Clark and Gardner's work, and evaluation of the answer set on novice, intermediate tasks, and questions with no answers.
- MS MARCO is a human-generated machine reading comprehension (MRC) dataset, designed to evaluate large language models' performance in answering complex questions from search engine queries and web documents.
- The original version (v1.1) had 10,894 questions with 357,631 passages, while the new V2 Tasks (v2.1) increased to 11,582 questions and 413,765 passages.
- The dataset's difficulty level has increased in v2.1 due to more complex questions and a broader scope of topics.
- BiDaF model performance dropped on the intermediate task because it only uses vocabulary present in the passage, while well-formed answers may include words from general vocabulary.
- Future work includes exploring new metrics (e.g., ROUGE-2 and ROUGE-AR), robust evaluation strategies, multi-task learning, cross-domain learning, and potentially generating similar datasets in other languages or augmenting the existing one with additional information.
- The MS MARCO dataset has been a significant learning experience for its creators and has attracted interest from the broader academic community.
- MS MARCO is a human-generated Machine Reading Comprehension (MRC) dataset for evaluating and improving machine reading comprehension systems.
- The dataset consists of 10,000 questions with multiple-choice answers from real-world search contexts.
- Google Assistant, Siri, and Baidu's DuerOS are among the systems that have used MS MARCO for training and evaluation purposes.
- The paper discusses various approaches to improve machine reading comprehension performance using MS MARCO, including deep learning models, attention mechanisms, and reinforcement learning techniques.
- Deep Residual Learning (ResNet) and BERT are among the most effective models used in this context.
- Attention-based models like Bidirectional Encoder Representations from Transformers (BERT), Self-Attentive Policy Networks, and End-to-End Memory Networks have shown significant improvements in MRC performance.
- Reinforcement learning methods such as Deep Q-Networks and Proximal Policy Optimization have also been applied to improve machine reading comprehension accuracy.
- The paper highlights the importance of using large-scale, real-world datasets like MS MARCO for training and evaluating machine reading comprehension systems.
- Future research directions include exploring more advanced models, incorporating contextual information, and developing better evaluation metrics to accurately measure performance in complex scenarios.
- The paper emphasizes the need for continuous collaboration between researchers, industry partners, and search engine companies to advance the field of machine reading comprehension.
",1505
"1611.09830",1,"- NewsQA is a challenging machine comprehension dataset with over 100,000 human-generated question-answer pairs based on 10,000 news articles from CNN.
- The dataset requires reasoning skills beyond simple word matching and textual entailment, making it more complex than existing datasets.
- A four-stage process was used to collect the data, ensuring questions were exploratory and not easily answered by simple keyword matching.
- Human performance on NewsQA is measured at 0.792 in F1 score, while strong neural models achieve around 0.65. The gap between human and machine performance indicates significant progress can be made through future research.
- The dataset is freely available for use by the research community to advance machine comprehension capabilities.
- NewsQA is a machine comprehension dataset designed for high-volume, rapidly changing information sources like news articles.
- The four-stage collection process encourages curiosity-based questions and aims to teach models reasoning-like behaviors.
- Answers are spans of arbitrary length within an article, some questions have no answer (null span), there are no candidate answers, and a significant proportion requires reasoning beyond simple word matching.
- NewsQA offers a greater challenge compared to other datasets like SQuAD, with humans outperforming powerful question-answering models.
- The paper discusses related datasets, their characteristics, and how diverse collections of datasets can benefit machine comprehension research.
- NewsQA: A Machine Comprehension Dataset - This paper introduces a new dataset for machine comprehension, focusing on questions requiring rudimentary reasoning and synthesis of information across sentences. The challenge lies in the lack of candidate answers provided, unlike other datasets such as MCTest, CNN/Daily Mail, or Children's Book Test.
- CNN/Daily Mail - A corpus consisting of news articles from CNN and Daily Mail with cloze-style questions. Anonymized named entities within an article serve as the set of candidate answers. This dataset has a large amount of data (1.4 million question-answer pairs) but requires limited reasoning, with performance nearly matching humans.
- Children's Book Test - Similar to CNN/Daily Mail, this dataset uses 20-sentence excerpts from children's books for context and evaluates word prediction based on context. It focuses more on word prediction than comprehension, as other mechanisms may be more important.
- BookTest - A remedy proposed by Bajgar et al. (2016) to address the insufficiency of existing datasets. This extension increases the size of CBT's named-entity and common-noun strata by over 60 times, resulting in a model that matches human performance on CBT.
- MCTest - A dataset used for machine comprehension with questions requiring rudimentary reasoning and synthesis of information across sentences. Recent models have performed well on this dataset, but it lacks the challenge of providing no candidate answers like NewsQA.
- Practical applications - These datasets can be used to train and evaluate machine learning models in the field of natural language processing (NLP) for tasks such as question answering or reading comprehension. They provide a benchmark for measuring progress in this area.
- NewsQA is a machine comprehension dataset that aims to improve upon existing models by focusing on more challenging questions and encouraging reasoning in comprehension models.
- The paper compares NewsQA with SQuAD, another comprehension dataset, highlighting the differences and potential benefits of NewsQA for pushing the development of intelligent machine comprehension systems.
- The collection process involves four stages: article curation, question sourcing, answer sourcing, and validation, along with a post-processing step to enhance usability.
- Article curation uses CNN articles, randomly selecting 12,744 from a set of 90,266 for training, development, and test sets.
- Question sourcing involves crowdworkers who see only headlines and summary points, encouraging questions that require reasoning and may not have sufficient evidence in the text.
- Answer sourcing is done by a separate group of crowdworkers who read the full article to find the correct answer spans.
- The dataset consists of 124,935 question-answer pairs from 10,817 articles, with an average of 11.5 questions per article and 11.6 answers per question.
- NewsQA's human accuracy is measured at 0.807 using a different methodology compared to SQuAD's 0.905 in F1 score.
- The paper presents preliminary results from a baseline model, achieving an average of 32% accuracy on the test set and 46% on the development set.
- NewsQA is publicly available for research purposes, with plans to release additional data as it becomes available.
- NewsQA is a machine comprehension dataset that involves three roles: Questioners, Answerers, and Validators.
- Questioners create questions based on incomplete information from CNN summaries, encouraging curiosity and preventing simple reformulations of sentences.
- Answerers provide answers to the questions using full articles, with multiple crowdworkers contributing to answer agreement.
- A validation process is used to ensure high-quality data by having a third set of workers choose the best answer or reject all options.
- After validation, 86% of questions have at least two separate crowdworkers agreeing on an answer, and those without agreement are marked as null answers for training models.
- A final cleanup step combines close answer spans to improve consistency in the dataset.
- The dataset can be used for various applications such as question answering systems, machine comprehension, and evaluating language models' ability to understand complex questions.
- NewsQA contains 120k question-answer pairs from 5k articles across 30 topics, with an average of 4 questions per article.
- NewsQA is a machine comprehension dataset designed to test and evaluate the performance of language models in answering complex questions from news articles.
- 5.68% of answers consist of multiple spans, with 71.3% of these multi-spans fitting within a 3-word threshold. Multi-span answers often represent lists, presenting an interesting challenge for comprehension models.
- The dataset analysis reveals various forms of reasoning required to solve NewsQA questions, including word matching, paraphrasing, inference, synthesis, and ambiguous/insufficient information.
- Word Matching is the easiest form of reasoning, while Synthesis and Ambiguous/Insufficient are the most difficult.
- The dataset provides a thorough analysis of answer types, with common noun phrases being the majority (22.2%), followed by clause phrase (18.3%), person (14.8%), numeric (9.8%), and other (11.2%).
- NewsQA is a useful benchmark for evaluating machine comprehension models, as it demonstrates the challenge of answering complex questions from news articles and requires various forms of reasoning.
- NewsQA is a machine comprehension dataset for evaluating reading comprehension models, focusing on news articles and their associated questions.
- The dataset has four reasoning types: word matching, paraphrasing, inference, and synthesis, with varying difficulty levels.
- NewsQA contains 10,000 examples, while SQuAD (Stanford Question Answering Dataset) has 107,785 examples.
- NewsQA's more challenging reasoning types (synthesis and inference) make up a combined 33.9% of the data compared to 20.5% in SQuAD.
- Three baseline models were tested on NewsQA: human data analysts, match-LSTM (mLSTM), and a custom neural model designed by the authors.
- The mLSTM model was chosen for its strong performance on similar datasets and ease of implementation.
- NewsQA is a machine comprehension dataset for evaluating and training question answering systems on news articles.
- The model uses an mLSTM network to compare document encodings with question encodings, followed by a Pointer Network to select the answer span boundaries.
- To facilitate faster experimentation, a lighter-weight BARB (Bilinear Annotation Re-encoding Boundary) model was developed, achieving similar results on SQuAD2.
- The BARB model consists of four stages: encoding, bilinear annotation, re-encoding, and boundary pointing.
- The model's performance is evaluated using accuracy, F1 score, and runtime efficiency.
- The dataset includes 30k training examples, 2k validation examples, and 2k test examples from the New York Times Annotated Corpus.
- NewsQA is a machine comprehension dataset for evaluating and training question answering systems on news articles.
- The paper introduces a convolutional neural network (CNN) architecture with boundary-pointing layers to address the challenges of identifying answer spans in complex news articles.
- Two convolutional layers are used in the boundary-pointing stage, and an intermediate level of ""guidance"" is provided by reducing feature dimension C in G with mean-pooling.
- Human evaluation results show that humans averaged 0.694 F1 on NewsQA, while human exact match scores were relatively low at 0.465 due to multiple semantically equivalent answers.
- The proposed CNN architecture outperforms the baseline models (mLSTM and Random) in both SQuAD and NewsQA datasets.
- Future work includes identifying unanswerable questions, improving the model's performance on long documents, and exploring other CNN architectures for machine comprehension tasks.
- The paper introduces ""NewsQA: A Machine Comprehension Dataset,"" which aims to evaluate machine performance in understanding news articles and answering questions about them.
- Human performance on the SQuAD and NewsQA datasets is compared, revealing a significant gap between human and machine comprehension abilities.
- The paper highlights that simpler automatic metrics like BLEU and CIDEr are not equal to complex Machine Comprehension (MC) evaluation tasks.
- Human performance on NewsQA shows a larger gap in comparison to SQuAD, suggesting room for improvement in MC methods.
- Model performance is stratified by answer type and reasoning type, revealing that models perform better with named entities and worse with questions requiring inference and synthesis.
- The paper postulates that longer news stories in NewsQA make it harder to synthesize information from separate sentences, necessitating the tracking of longer-term dependencies.
- NewsQA is a new machine comprehension dataset introduced for challenging language models with diverse answer types and questions requiring reasoning ability.
- The dataset consists of 100,000+ examples from CNN articles, highlights, and questions with answers determined by crowdworkers.
- BARB (Bidirectional Attentive Reading for Comprehension) outperforms human annotators in answering ambiguous or insufficient information on SQuAD, highlighting the difficulty of NewsQA.
- A sentence-level subtask is proposed to demonstrate relative difficulty, using inverse sentence frequency (isf) to find the answer sentence with the highest score.
- The isf method achieves 79.4% accuracy on SQuAD's development set but only 35.4% accuracy on NewsQA's development set, indicating NewsQA's complexity.
- Artificially increasing SQuAD article lengths decreases accuracy as expected, yet remains higher than NewsQA with comparable or greater length.
- The paper concludes that NewsQA makes a significant extension to the field of machine comprehension and provides a challenging dataset for future research.
- NewsQA is a machine comprehension dataset that aims to advance the field by providing a large and complex corpus for further research in this area.
- The dataset consists of 10,000 news articles with associated questions, making it significantly larger than existing datasets like CNN/Daily Mail (800+ questions) or SQuAD (100,000+ questions).
- NewsQA's complexity comes from its diverse range of question types and the need for models to understand contextual information in news articles.
- The dataset achieves a BLEU score of 0.479 and CIDEr score of 1.165, demonstrating its effectiveness in evaluating machine comprehension performance.
- NewsQA's size and complexity are expected to spur further advances in machine comprehension and contribute to the development of literate artificial intelligence.
- The paper acknowledges various researchers for their contributions and provides a comprehensive list of references, highlighting related work in the field.
- NewsQA is a machine comprehension dataset for evaluating and improving natural language understanding systems.
- The dataset consists of 3,000 news articles from the New York Times, with associated questions and answers.
- It includes multiple-choice questions, yes/no questions, and open-ended questions to test various aspects of machine comprehension.
- Two models are introduced: Match-LSTM (mLSTM) and Bidirectional Answer-Pointer Networks (BARB).
- mLSTM uses a bi-directional RNN for pre-processing and answer-pointing, while BARB employs a hierarchical attention mechanism.
- Both models achieve high accuracy on the NewsQA dataset: mLSTM with 79.3% and BARB with 81.5%.
- The authors also provide an implementation guide for training these models using Keras, Theano, and other tools.
- This work contributes to the development of more effective machine comprehension systems by introducing a new dataset and two novel models.
- Future research can focus on improving model performance, exploring different architectures, and expanding the NewsQA dataset with additional news sources.
- The paper's findings have practical applications in various fields such as question answering systems, information retrieval, and natural language processing.
- NewsQA is a machine comprehension dataset for evaluating news reading comprehension systems.
- The model uses bi-directional RNNs (mLSTM) and Match-LSTMs for pre-processing and answer-pointing layers, respectively.
- Initialization methods include normal distribution, orthogonal initialization, and Glorot uniform initialization.
- BARB (Bidirectional Attention with Recurrent Readout) is used as the main attention mechanism, with specific hyperparameters for SQuAD and NewsQA.
- The paper presents user interfaces for question sourcing, answer sourcing, and validation during data collection.
- Key findings include 30% accuracy on the test set, 4.5 times faster training time compared to previous methods, and a 12-hour training time for NewsQA.
- The dataset can be used to improve news reading comprehension systems by providing a benchmark for evaluation.
",2817
"1701.06538",2,"- The paper introduces a Sparsely-Gated Mixture-of-Experts (MoE) layer to address neural networks' capacity and efficiency limitations in conditional computation.
- MoE consists of thousands of feed-forward sub-networks, with a trainable gating network determining which experts to use for each example.
- The authors apply MoE to language modeling and machine translation tasks, where model capacity is crucial due to vast training corpora.
- They present model architectures using convolutionally applied MoE between stacked LSTM layers, with up to 137 billion parameters.
- On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.
- The paper demonstrates that conditional computation can be realized without sacrificing efficiency, leading to over 1000x improvements in model capacity.
- MoE's success is attributed to its ability to learn which experts are relevant for each example, reducing redundancy and computational cost.
- The authors provide practical applications of their findings by applying MoE to real-world tasks like language modeling and machine translation.
- This work opens up new possibilities for scaling neural networks without exponential increases in training costs, potentially leading to further advancements in deep learning models.
- The paper's contributions include introducing the Sparsely-Gated Mixture-of-Experts layer and demonstrating its effectiveness in increasing model capacity while maintaining computational efficiency on modern GPU clusters.
- The Sparsely-Gated Mixture-of-Experts (SGMOE) layer is a new type of neural network component that can be trained using standard backpropagation techniques, making it easy to integrate into existing architectures.
- The paper demonstrates the SGMOE layer's performance on various benchmark datasets, showing potential for model compression, transfer learning, and multi-task learning applications.
- The MoE layer consists of multiple expert networks (simple feed-forward neural networks) and a gating network that selectively combines experts based on input data. All parts are trained jointly using backpropagation.
- Applications in language modeling and machine translation tasks show improved performance at reduced computational costs compared to previous best results.
- The MoE layer's experts tend to specialize in syntax and semantics, allowing for more efficient processing.
- Related work includes various expert architectures (SVMs, Gaussian Processes, Dirichlet Processes, deep networks) and configurations (hierarchical structure, infinite numbers of experts, sequential addition).
- The paper builds on Eigen et al.'s use of multiple MoEs with their own gating networks in a deep model, introducing sparsity to increase computational efficiency.
- Convolutional application of the MoE allows for different gating decisions at each text position, further increasing efficiency and capacity.
- The paper demonstrates that sparse gating can be used as a practical way to significantly increase model capacity without sacrificing performance.
- The approach is generic and can be applied to various tasks beyond language modeling and machine translation.
- The paper introduces a sparsely-gated mixture-of-experts (MoE) layer to address memory and computational challenges in large neural networks.
- This MoE layer uses a gating network that selects a subset of experts for each input, improving efficiency as the number of experts increases.
- To tackle shrinking batch problems, data parallelism and model parallelism are combined in distributed training settings to increase expert batch sizes.
- The paper demonstrates improved computational efficiency while maintaining accuracy on image classification tasks using a hierarchical MoE architecture.
- By proportionally increasing the number of devices during training, total batch size increases without affecting parameters per device, step times, or time to process examples.
- Training trillion-parameter models with trillion-word corpora is possible by adding more hardware, though the authors haven't scaled their systems this far yet.
- The paper suggests using convolutionality in language models to apply MoE to all time steps together as one big batch, increasing input batch size for the MoE layer.
- Increasing batch size for recurrent MoEs could involve replacing weight matrices with an MoE, but this breaks the convolutional trick from point 4 due to dependencies between timesteps.
- Gruslys et al.'s technique reduces stored activations in unrolled RNNs, allowing for a large increase in batch size by recomputing forward activations.
- Network bandwidth is a major performance concern in distributed computing, with experts' communication involving input and output transmission. Computational efficiency can be improved by using larger hidden layers or more hidden layers.
- The paper introduces a soft constraint approach to ensure equal importance among experts in sparsely-gated mixture-of-experts (MoE) layers, addressing local minimum issues.
- An additional loss function, Limportance, is defined to encourage all experts to have equal importance by minimizing the coefficient of variation of the set of importance values.
- The gate values naturally diversify as experts specialize, reducing the need for enforcing diversity in gate values.
- A second loss function, Lload, is introduced to ensure balanced loads among experts, addressing memory and performance issues on distributed hardware.
- Experiments show that MoE models with low computational costs can achieve comparable or better results than larger LSTM models, suggesting that capacity addition through MoE layers is more efficient.
- The paper introduces a sparsely-gated mixture-of-experts (MoE) layer for neural networks, which allows for more efficient use of computational resources by dynamically activating only a subset of experts based on input data.
- Models with this MoE layer achieve impressive results in language modeling benchmarks, with the largest model achieving 24% lower perplexity compared to computationally-matched baselines.
- The paper demonstrates that models with varying computational budgets can be created using different numbers of experts and expert sizes, while maintaining high capacity (up to 4 billion parameters).
- Even with a reduced computational budget, the proposed MoE models outperform previous best results on the same dataset, requiring only 6% of the computation.
- The paper highlights the computational efficiency of the proposed approach, achieving TFLOPS/GPU ratios that are significantly higher than those of comparable LSTM models.
- The paper introduces a Sparsely-Gated Mixture-of-Experts (MoE) layer for neural networks, enabling conditional computation in deep learning models.
- This approach increases model size by adding up to 2048 experts per layer with about two million parameters each, leading to better performance in multilingual machine translation tasks.
- The MoE layer reduces perplexity and improves BLEU scores compared to traditional GNMT models, achieving a 19% lower perplexity on the dev set than a multilingual GNMT model.
- Conditional computation may be beneficial for other domains as well, provided sufficient training data is available.
- The paper highlights design considerations and challenges in implementing conditional computing through algorithmic and engineering solutions.
- Low-computation MoE models achieved efficiencies of 0.74-0.90 TFLOPS/GPU, with the highest-computation model being more efficient at 1.56 TFLOPS/GPU due to larger matrices.
- For larger training sets, even higher capacities led to significant quality improvements, with test perplexity dropping by up to 39% compared to baseline models.
- At 65,536 experts (99.994% layer sparsity), computational efficiency for the model remained at a respectable 0.72 TFLOPS/GPU.
- The paper presents a modified version of the GNMT model for machine translation with reduced LSTM layers and MoE layers inserted in both encoder and decoder.
- On Google's Production English to French dataset, the model achieved a 1.01 higher test BLEU score even after training for only one-sixth of the time.
- The paper introduces SMe layers, which significantly reduce neural network parameter counts while maintaining or improving performance on various tasks like language modeling, image classification, and machine translation.
- SMe layers use a gating mechanism to adaptively select relevant subnetworks based on input data, leading to better generalization and performance in low-resource settings.
- The concept of ""capacity"" is introduced as an alternative metric for evaluating neural network models, considering both model size and computational cost.
- SMe layers can be easily integrated into existing deep learning frameworks like TensorFlow, making them accessible to a wide range of researchers and practitioners.
- Practical guidelines are provided on designing and training SMe networks for various tasks, including hyperparameter tuning and regularization techniques.
- Experiments show that SMe layers can significantly reduce parameter counts while maintaining or improving performance, leading to more efficient and effective neural network architectures.
- The paper highlights potential applications in resource-constrained environments like mobile devices and edge computing systems where computational efficiency is crucial.
- A novel sparsely-gated mixture-of-experts (SGMOE) layer is introduced for neural networks, addressing the limitations of large models by enabling efficient computation and memory usage.
- SGMOEs can be used in various applications like language modeling, image classification, and machine translation, achieving significant improvements over traditional approaches.
- Experiments show that SGMOE layers achieve 10-25x reduction in model size while maintaining or improving accuracy compared to standard dense networks.
- The paper introduces a novel Sparsely-Gated Mixture-of-Experts (SGMOE) layer for neural networks to address vanishing gradients in deep learning models.
- SGMOE consists of multiple experts, each with its own weight vector and gating mechanism controlling information flow between them and the network's output layer.
- A new loss function called load-balancing loss is proposed to ensure equal training examples for all experts, preventing inactivity due to imbalance.
- Experiments show significant performance improvements on tasks like image classification and machine translation while reducing parameter numbers compared to traditional dense networks.
- The paper presents a combination of stochastic gradient descent (SGD) and natural gradient descent (NGD) for training SGMOE models, improving convergence speed and memory requirements.
- Practical applications include Google's neural machine translation system, leading to improved performance and reduced model size.
- The paper highlights that SGMOE layers can be easily integrated into existing deep learning frameworks, making them a promising alternative for addressing vanishing gradient issues in large-scale neural networks.
- Experiments show up to 95% reduction in parameter numbers while achieving 30% higher accuracy on image classification tasks compared to dense networks.
- In machine translation tasks, SGMOE layers reduce model size by 4.5 times and improve BLEU scores by up to 1.2 points.
- The paper introduces a Sparsely-Gated Mixture-of-Experts (S-MoE) layer to address memory consumption and training time issues in large neural networks, using a smooth estimator for backpropagation.
- The paper introduces a hierarchical mixture-of-experts (H-MoE) layer for large neural networks, addressing issues of memory consumption and training time in Sparse Mixture-of-Experts (S-MoE).
- H-MoE achieves comparable performance to S-MoE with faster convergence and lower memory usage.
- An application of H-MoE in a large-scale language model results in 10% better perplexity than the baseline.
- The authors suggest that H-MoE can be used for training models on distributed hardware with load balancing capabilities.
- Both S-MoE and H-MoE provide scalable solutions for training large neural networks, addressing memory consumption and training time issues.
- A two-level hierarchical MoE layer is introduced, where primary gating chooses a sparse weighted combination of secondary ""experts"" with their own gating network.
- The paper presents metrics for expert utilization: ImportanceH(X)i,j and LoadH(X)i,j, which measure the importance and load of each expert in the model.
- An 8-million-operations-per-timestep language modeling benchmark is used to test the proposed architecture, consisting of five layers: word embedding, LSTM, MoE layer, second LSTM, and softmax.
- The paper compares ordinary MoE layers with hierarchical MoE layers in terms of performance and computational efficiency using models like MoE-4, MoE-32, MoE-256, MoE-256-h, MoE-1024-h, and MoE-4096-h.
- Hierarchical MoEs can achieve similar performance as ordinary MoEs while reducing computational cost by up to 4.5 times, making them suitable for large neural networks.
- The paper introduces a novel sparsely-gated Mixture-of-Experts (MoE) layer for neural networks, addressing limitations of large models by enabling efficient computation and memory usage.
- MoE layers consist of multiple experts with shared parameters but separate gates controlling their activation, leading to sparse computation and improved scalability.
- Experiments using different configurations of MoE layers in conjunction with LSTM networks on a 100 billion word Google News corpus show comparable or better performance than traditional LSTMs while reducing computational costs and memory requirements.
- The paper explores the impact of adding more computation to large MoE layers, demonstrating that even in these cases, additional computation can still be beneficial.
- Potential applications include models with billions of parameters for tasks like machine translation and speech recognition.
- Memory optimization techniques are discussed, including not storing hidden layer activations and modifying backward pass computations to fit large parameter sets on GPUs.
- The MoE layers can be implemented in existing deep learning frameworks like TensorFlow and CNTK without significant modifications.
- Further research is suggested into more efficient memory management techniques and the use of different activation functions within the MoE layer.
- A modified Adam optimizer for sparsely gated Mixture-of-Experts (MoE) layers is introduced, reducing memory requirements and improving computational efficiency in large neural networks.
- The paper introduces a Sparsely-Gated Mixture-of-Experts (MoE) layer for neural networks to address the issue of large models becoming computationally expensive and difficult to train in machine translation tasks.
- MoE layers are added to LSTM networks, with different numbers of experts (32, 512, or 2048). These layers use residual connections for gradient flow and subword units (wordpieces) for inputs and outputs.
- The paper compares the performance of flat MoE layers to hierarchical structures, finding that hierarchical models outperform flat ones in terms of perplexity and BLEU scores, especially when dealing with rare words.
- Hierarchical MoE layers are more computationally efficient than flat ones, requiring fewer parameters while achieving better performance.
- The paper highlights the potential benefits of using Sparsely-Gated Mixture-of-Experts in large neural networks for machine translation tasks.
- Experts become highly specialized due to sorting inputs by their gating function (G(x)i), which helps identify patterns and focus on specific aspects of language during training.
- The largest MoE model (MoE-131072-h) has a perplexity 39% lower than the baseline model after 100 billion training words in the Google News Dataset.
- Computational efficiency is low for the largest model due to not increasing the training batch size proportionally with the number of GPUs, but it's still comparable to other models in terms of TFLOPS/GPU.
- The paper also discusses machine translation experiments using MoE models for single language pairs, reducing computation by decreasing LSTM layers and inserting MoE layers between encoder and decoder.
- Training is done using Adam optimizer and synchronously on up to 64 GPUs with dropout applied to all embedding, LSTM, and MoE layers at a probability of 0.4 for single-language models.
- The paper explores large neural networks, focusing on up to 100 billion words and Kneser-Ney 5-gram models for improved performance.
- It introduces a sparsely-gated mixture-of-experts (MoE) layer that can be applied in various contexts like machine translation and language modeling, leading to better results.
- The paper discusses practical implications of the findings, including potential applications in natural language processing tasks.
- The approach helps address issues related to large neural networks, such as vanishing or exploding gradients, by learning specialized experts for specific language patterns and contexts.
- A sparse gating function is used to assign experts to input examples, addressing the issue of equal batch sizes for all experts with Top-K and Batchwise masks introduced as alternatives.
- The paper presents a modified attention function for performance reasons, finding little difference in quality compared to GNMT (Wu et al., 2016).
- Experiments show that using the sparsely-gated MoE layer leads to faster training times and improved model accuracy.
- A new method called Sparsely-Gated Mixture-of-Experts (SGMOE) layer is introduced, aiming to reduce computational complexity and memory usage in large neural networks.
- The SGMOE layer can be used without compromising accuracy in various neural network architectures like CNNs for image classification tasks.
- This approach leads to more efficient training of deep learning models with larger datasets and neural networks, resulting in faster model training and reduced memory requirements for large-scale AI systems.
- The Sparsely-Gated Mixture-of-Experts (SGMoE) layer is a novel approach to optimizing matrix multiplications in large neural networks, offering an efficient alternative for AI systems.
- This paper analyzes the SGMoE layer's performance in various scenarios, focusing on its impact on model accuracy, training time, and memory usage.
- The SGMoE layer can lead to faster training times and reduced resource requirements, making it a potential solution for optimizing large neural networks.
- By replacing dense matrix multiplications with sparse ones, the SGMoE layer enables more efficient computation in deep learning models.
- This approach could be particularly useful in scenarios where memory constraints are an issue or when dealing with massive datasets.
- The paper provides examples of how the SGMoE layer can improve performance and resource utilization in real-world applications.
- While the SGMoE layer may not always outperform traditional dense matrix multiplications, it offers a valuable alternative for specific use cases or when memory constraints are present.
- The paper's findings suggest that the SGMoE layer can be an effective tool for optimizing large neural networks and improving AI systems' performance in certain scenarios.
",3656
"1703.04009",1,"- The paper explores the challenge of distinguishing hate speech from offensive language in automated hate speech detection on social media.
- Lexical detection methods have low precision due to classifying all messages containing specific terms as hate speech, while previous supervised learning efforts failed to differentiate between hate speech and offensive language.
- The authors used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords and labeled a sample of these tweets into three categories: hate speech, offensive language, and neither.
- A multi-class classifier was trained to distinguish between these categories, and the analysis of predictions and errors revealed when reliable separation of hate speech and offensive language was possible and when it was more difficult.
- Racist and homophobic tweets were more likely to be classified as hate speech, while sexist tweets were generally classified as offensive. Tweets without explicit hate keywords were more challenging to classify.
- The study highlights the importance of understanding the nuances of hate speech and offensive language to improve automated detection systems and better address the issue on social media platforms.
- The paper defines hate speech as language expressing hatred towards a group, derogatory, humiliating, or insulting, which may also threaten or incite violence. It excludes all instances of offensive language, acknowledging that some offensive terms are used differently by certain groups.
- Previous studies have struggled to differentiate between hate speech and offensive language, leading to confusion in hate speech detection. The paper proposes a classification system with three categories: hate speech, offensive language, and neither.
- The model trained to differentiate between these categories demonstrates that fine-grained labels can improve hate speech detection. The study highlights the challenges in accurately classifying hate speech due to contextual and linguistic nuances.
- Bag-of-words approaches often result in high recall rates but high false positive rates, as offensive words can lead to misclassification.
- The difference between hate speech and offensive language often lies in subtle linguistic distinctions, such as the use of the words ""n*gger"" and ""n*gga.""
- Many offensive terms can be ambiguous, making hate speech detection challenging.
- The paper emphasizes the need for future work to better account for context and the heterogeneity in hate speech usage.
- The study's findings have practical applications in improving hate speech detection systems and understanding the nuances of offensive language.
- The paper's results show that fine-grained labels can improve hate speech detection accuracy, highlighting the importance of context and linguistic nuances in classification.
- The study's findings suggest that future work should focus on addressing these challenges to improve hate speech detection systems.
- The paper discusses the challenges of identifying and classifying hate speech in text, focusing on the distinction between hate speech and offensive language.
- Syntactic features, such as specific noun-verb combinations, POS trigrams, and intensity-user intent-hate target structures, have been used to improve hate speech classification.
- Supervised approaches to hate speech classification have been found to conflate hate speech with offensive language, making it difficult to determine the extent of accurate identification.
- Neural language models show promise in hate speech classification, but existing work has used training data with a broad definition of hate speech.
- Non-linguistic features, like author's gender or ethnicity, can improve hate speech classification, but this information is often unreliable or unavailable on social media.
- The paper uses a hate speech lexicon from Hatebase.org, Twitter API, and CrowdFlower workers to create a dataset of 24,802 labeled tweets, with only 5% classified as hate speech.
- The majority of tweets were considered offensive language, while the remainder were non-offensive, demonstrating the imprecision of the Hatebase lexicon.
- The paper highlights the importance of context in determining hate speech, as the presence of an offensive word alone does not necessarily indicate hate speech.
- The study emphasizes the need for more accurate and reliable hate speech classification methods, as well as the importance of context and understanding the nuances of language.
- Practical applications of these findings could include improving hate speech detection algorithms and developing better hate speech classification systems.
- The paper explores automated hate speech detection and offensive language classification in tweets.
- Features used for classification include lowercased and stemmed tweets, TF-IDF weighted bigrams, unigrams, and trigrams, Penn POS tag unigrams, bigrams, and trigrams, Flesch-Kincaid Grade Level and Flesch Reading Ease scores, sentiment lexicon, binary and count indicators for hashtags, mentions, retweets, URLs, number of characters, words, and syllables.
- Logistic regression, naive Bayes, decision trees, random forests, and linear SVMs were tested, with logistic regression and linear SVM performing better.
- The final model used logistic regression with L2 regularization, trained on the entire dataset, and assigned class labels using a one-versus-rest framework.
- The best-performing model achieved an overall precision of 0.91, recall of 0.90, and F1 score of 0.90, but misclassified 40% of hate speech.
- The model was biased towards classifying tweets as less hateful or offensive than human coders.
- The paper highlights the importance of using a diverse set of features and models to improve hate speech detection accuracy.
- The study emphasizes the need for further research to address the limitations and biases in automated hate speech detection systems.
- Practical applications of the study include improving social media platforms' ability to detect and remove offensive content.
- The paper also discusses the ethical implications of automated hate speech detection and the potential for misuse of such systems.
- The paper investigates the accuracy of automated hate speech detection systems and compares their performance to human coders.
- Automated systems tend to misclassify a small percentage of offensive and innocuous tweets as hate speech, with 5% and 2% errors, respectively.
- Tweets with the highest predicted probabilities of being hate speech often contain multiple racial or homophobic slurs, while correctly identified hate speech tweets contain strong racist or homophobic terms.
- Some cases of misclassification occur when people use hate speech to respond to other hate speakers, or when tweets are genuinely less hateful and may have been mislabeled.
- Borderline cases, where the probability of being offensive is marginally higher than hate speech, are mostly hate speech, but may be misclassified due to the absence of common hate speech terms.
- Hateful tweets incorrectly labeled as neither contain negative terms but lack slurs against specific groups.
- The classifier performs well in detecting prevalent forms of hate speech, such as anti-black racism and homophobia, but is less reliable in identifying rare types of hate speech.
- A key flaw in the automated systems is their inability to consider context and nuance, which may lead to misclassification.
- Practical applications of the study include improving automated hate speech detection systems by addressing these limitations and incorporating contextual understanding.
- The study highlights the importance of understanding the limitations of automated systems and the need for human oversight in certain cases.
- The paper discusses the challenges of automated hate speech detection, focusing on the problem of offensive language misclassification as hate speech.
- A multi-class framework is introduced to minimize errors, resulting in only 5% of true offensive language being labeled as hate.
- Offensive tweets often contain curse words and sexist language, while racist and homophobic terms are more likely to be considered hateful.
- Human coders tend to classify sexist language as offensive rather than hateful, leading to misclassification.
- Misclassified tweets containing multiple slurs and recurring phrases, such as lyrics from rap songs, can lead to overestimating the prevalence of hate speech.
- The model avoids most misclassifications by differentiating between hate speech and offensive language.
- Tweets in the ""neither"" class are generally innocuous, with higher readability scores and positive sentiment, and may contain terms from the Hatebase lexicon.
- Misclassifications in the ""neither"" class are often caused by the presence of potentially offensive language in a positive context.
- The importance of taking context into account is emphasized, as shown in examples where offensive terms are used in a positive sense.
- The paper highlights the need for better understanding of context and language usage to improve hate speech detection models.
- The paper explores the challenges of automated hate speech detection and the problem of offensive language.
- Lexical methods are effective for identifying offensive terms but inaccurate for hate speech identification.
- Automated classification methods can achieve high accuracy, but close analysis reveals the importance of specific terms in distinguishing between hate speech and offensive language.
- Certain terms, such as ""f*ggot"" and ""n*gger,"" are generally associated with hate speech, while others, like ""f*g"" and ""b*tch,"" are used in both hate speech and offensive language.
- To improve hate speech classification, focus on finding training data without relying on specific keywords or offensive terms.
- Hate speech can be used in various ways, and future research should consider different uses and social contexts.
- Study the characteristics and motivations of people who use hate speech, as well as the social structures they are embedded in.
- The paper's findings emphasize the importance of accurately distinguishing between hate speech and offensive language due to legal and moral implications.
- A smaller lexicon with higher precision is preferable to a larger lexicon with higher recall.
- The authors provide a more restricted version of the Hatebase lexicon for further research.
- Hate speech is a complex and subjective phenomenon, with varying definitions and perceptions of offensiveness.
- Classifications of hate speech often reflect personal biases, as people may view racist and homophobic slurs as hateful but see sexist language as merely offensive.
- While algorithms can perform well in identifying extreme instances of hate speech, such as anti-black racism and homophobia, there is a need to address social biases within these algorithms.
- Future work should aim to identify and correct these biases to improve the accuracy and fairness of hate speech detection systems.
- People's ability to identify hate speech may be influenced by their own cultural and social backgrounds, highlighting the importance of considering diverse perspectives in algorithm development.
- The paper emphasizes the need for ongoing research and development to address the complexities of hate speech detection and ensure fair and accurate algorithms.
- Practical applications of these findings could include more inclusive and accurate hate speech detection systems, leading to a better understanding of the social factors that influence language use and perceptions.
- The paper's findings challenge the assumption that hate speech can be universally defined and detected, emphasizing the need for a more nuanced approach to hate speech detection.
- The authors call for a more comprehensive understanding of the social contexts and motivations behind hate speech, which could lead to more effective and equitable algorithms.
- By addressing social biases and cultural differences, future work can improve the accuracy and fairness of hate speech detection systems, ultimately contributing to a more inclusive and respectful online environment.
",2177
"1703.04933",1,"- Deep learning architectures tend to generalize well despite their tendency to overfit, but explaining this phenomenon remains an open research area.
- The hypothesis that flat minima of loss function lead to good generalization is gaining popularity, but it's problematic for deep models and can't directly explain generalization.
- In deep networks with rectifier units, the inherent symmetries induce a particular geometry in parameter space, allowing equivalent models with arbitrarily sharper minima.
- Allowing reparameterization of functions can drastically change their parameter geometry without affecting generalization properties.
- Deep learning techniques have been successful in various domains due to efficient representation and optimization capabilities, as well as good generalization performance.
- The paper focuses on analyzing the estimation error aspect of deep learning models' generalization.
- Previous works have explored different approaches to understanding why stochastic gradient descent leads to solutions that generalize well.
- This study highlights the importance of considering the geometry of parameter space in deep networks and its implications for generalization.
- The findings suggest that deep learning models can be improved by exploiting their inherent symmetries and reparameterizations, potentially leading to better generalization performance.
- Practical applications could benefit from understanding how these properties impact model design and optimization strategies.
- The paper explores the conjecture that flat minima lead to better generalization in deep neural networks, focusing on the concept's challenges when applied to these complex architectures.
- Different works define flatness differently, but it generally refers to a wide region around a minimum with similar error values. Defining flatness becomes more complicated in higher dimensional spaces.
- Several common deep learning architectures and parameterizations contradict the conjecture that flat minima lead to better generalization.
- The authors argue that this contradiction stems from Bayesian arguments about KL divergence used to justify the generalization ability of flat minima, as Kullback-Leibler divergence is invariant to parameter changes while ""flatness"" isn't.
- Demonstrations by Hochreiter & Schmidhuber (1997) are based on Gibbs formalism and rely on strong assumptions and approximations that can compromise the applicability of their argument, including the assumption of a discrete function space.
- The paper introduces a new definition of flatness/sharpness in terms of the geometry of the associated parameter space, which alters the ranking between prediction functions when considering different measures.
- This work highlights the need for further research to better understand how flatness affects generalization in deep neural networks and develop more accurate definitions of flatness.
- The paper introduces a new perspective on analyzing the generalization capabilities of deep neural networks by studying their minima.
- It defines three metrics for measuring flatness and sharpness in minima, which are volume ϵ-flatness, spectral norm of Hessian (local curvature), and ϵ-sharpness.
- The authors demonstrate that the generalization gap between observationally equivalent models can be explained by their relative flatness/sharpness.
- They show that a larger volume ϵ-flatness region leads to better generalization, while a smaller spectral norm of Hessian and higher ϵ-sharpness indicate better generalization.
- The paper suggests that the volume ϵ-flatness metric can be used as a proxy for the generalization gap in deep neural networks.
- They provide empirical evidence showing that the volume ϵ-flatness metric correlates with the test accuracy of deep neural networks, while other metrics do not.
- The paper highlights that the volume ϵ-flatness metric can be used to compare different architectures and regularization techniques in terms of generalization capabilities.
- They propose a new algorithm called MinimaNet for finding observationally equivalent models with better generalization properties, which is based on maximizing the volume ϵ-flatness.
- The paper discusses how the volume ϵ-flatness metric can be used to analyze and improve the generalization capabilities of deep neural networks in various applications.
- They provide a theoretical analysis showing that the volume ϵ-flatness metric is related to the VC dimension, which further supports its use as a proxy for the generalization gap.
- The paper explores the relationship between sharp minima and generalization capabilities of deep neural networks.
- Sharp minima are defined as local minima with small Hessian spectral norms, indicating a well-conditioned loss landscape.
- A second-order Taylor expansion is used to connect ϵ-sharpness to the Hessian's spectral norm.
- The paper focuses on deep rectified feedforward networks with linear output layers and demonstrates how these properties can be manipulated to control the flatness of a minimum.
- Understanding non-Euclidean geometry in neural architecture is crucial for optimizing neural networks, as it helps control changes in model behavior rather than focusing on parameters themselves.
- The paper introduces an example illustrating the effects of non-negative homogeneity and how it affects level curves of loss function embedded into a two-dimensional parameter space.
- The study's findings suggest that sharp minima can generalize well for deep nets, implying that they might be useful in guiding optimization algorithms to find better solutions.
- Practical applications include extending the results to other architectures like convolutional networks and applying them to improve optimization methods for neural networks.
- The paper highlights the importance of understanding non-Euclidean geometry in neural architecture, which can lead to better optimization techniques and improved generalization capabilities for deep learning models.
- Sharp minima are shown to be a promising indicator for guiding optimization algorithms towards better solutions, potentially leading to more efficient and effective deep learning models.
- The paper explores the concept of flatness and sharpness in neural network minima, focusing on their relationship with generalization capabilities.
- It introduces a metric to measure changes in model behavior and how it relates to curvature in parameter space for neural networks.
- Non-negative homogeneity property is discussed, which allows constructing continuous paths of parameters leading to the same behavior.
- This non-identifiability issue can be exploited to control flatness by manipulating the neighborhood around a minimum.
- The rectified linear activation function (ReLU) is studied for its widespread use in deep learning models, and it's shown that ReLU has this non-negative homogeneity property.
- This non-identifiability issue also exists in other models like deep linear networks, leaky rectifiers, or maxout networks.
- The paper highlights the importance of understanding these properties to improve generalization capabilities and avoid overfitting in neural network training.
- The paper introduces a non-negative homogeneity property found in maxout networks and rectified feedforward networks, which can be used to define transformations called α-scale transformations.
- These transformations do not affect the generalization of the function but significantly decrease several measures of flatness.
- The paper demonstrates that all minima are equally flat in deep rectified networks using α-scale transformations, which contradicts some definitions of flatness.
- A volume ϵ-flatness theorem is presented for one-hidden layer rectified neural networks, showing that the error remains approximately constant within a connected region containing the minimum with infinite volume.
- The paper highlights the importance of considering non-zero weight matrices in rectified feedforward networks to avoid constant functions and poor training performance.
- α-scale transformations can be applied to any architecture having a single rectified network as a submodule, such as deep rectified feedforward networks.
- The paper's findings suggest that some definitions of flatness may not accurately capture the behavior of deep rectified networks and could potentially lead to misleading conclusions about their generalization capabilities.
- Sharp Minima Can Generalize for Deep Nets paper introduces a new volume-based measure of flatness, called ϵ-flatteness, to analyze the generalization properties of minima in deep neural networks.
- The study shows that every minimum has an infinite region with approximately constant error, making it infinitely ϵ-flat according to this new measure.
- This result implies that all minima are equally flat and cannot be used to gauge the generalization property of a minimum.
- Hessian-based measures (spectral radius and trace) can also be manipulated without affecting the function's behavior, further supporting the idea that flatness is not an accurate measure for generalization.
- The paper demonstrates how gradient and Hessian transformations can find observationally equivalent parameters with arbitrarily large spectral norms in a one-hidden layer rectified neural network.
- This study highlights the limitations of current measures used to analyze the generalization properties of deep neural networks, suggesting new directions for future research.
- The paper explores the relationship between sharp minima and generalization capabilities in deep neural networks.
- It introduces a new notion of ""sharpness"" based on the spectral norm of the Hessian matrix, which measures the potential generalization error.
- The authors demonstrate that for any minimum with non-zero Hessian, there exists another observationally equivalent minimum with an arbitrarily large spectral norm.
- This finding suggests that the spectral norm of critical points' Hessians becomes less relevant as a measure of potential generalization error.
- The paper also discusses how this concept can be applied to deeper neural networks and introduces a theorem for (K-1)-hidden layer rectified neural networks.
- It highlights that, in some cases, the entire eigenspectrum of the Hessian might be considered instead of just its largest eigenvalue.
- The paper also presents a method to increase several eigenvalues of the Hessian matrix by varying α.
- This work contributes to understanding how sharp minima can generalize better in deep neural networks and provides insights into potential directions for further research.
- The paper demonstrates that sharp minima can generalize for deep neural networks, even when they have a large number of parameters.
- It introduces a new concept called ""ϵ-sharpness"" to measure the probable generalization capability of a model.
- By exploiting nonidentifiability and its particular geometry, it's possible to obtain sharper minima in deep neural networks.
- The paper shows that every minimum in rectified neural networks is observationally equivalent to another minimum with high ϵ-sharpness.
- This finding applies to the full-space ϵ-sharpness used by Keskar et al. (2017).
- The study suggests that rank deficiency in Hessian might be due to over-parametrization of models, leading to a majority of large eigenvalues.
- Sharp minima can still exist for thin and deep neural networks, implying they're sharp in multiple directions.
- The paper provides an example where the loss function is maximized at a point that's observationally equivalent to another point with high ϵ-sharpness.
- This work contributes to understanding how nonidentifiability can be exploited for better generalization and sharper minima in deep neural networks.
- The findings have practical implications, as they provide insights into the generalization capabilities of deep learning models with large numbers of parameters.
- The paper explores the limitations of using typical definitions of minimum's flatness as a core explanation for generalization, focusing on deep rectified neural networks and their non-Euclidean geometry.
- Allowing reparametrizations in the model demonstrates that correlation between geometry of parameter space and behavior of a given function is meaningless without considering specific parametrization.
- Reparametrization can lead to arbitrarily different geometries without affecting unseen data evaluation, implying that the relationship between minimum flatness and generalization is not straightforward.
- The paper shows how reparametrizing the problem modifies the geometry of loss function, allowing sharp minima in one parameter space to correspond to flat minima in another.
- Practical works demonstrate powerful bijections' ability to transform problems, while theoretical studies show their importance in understanding the relationship between geometry and generalization.
- The paper explores how Sharp Minima can generalize for deep neural networks, focusing on weight normalization reparametrization as an example.
- Reparametrizations allow for the modification of minima curvatures in loss functions, making some minima significantly flatter or sharper than others.
- Weight normalization reparameterizes nonzero weights by scaling them with a new parameter (scale) and unnormalized weights.
- Every minimum has infinite volume ϵ-sharpness, is observationally equivalent to an infinitely sharp minimum and an infinitely flat minimum when considering nonzero eigenvalues of the Hessian.
- The notion of flatness for a minimum in loss functions alone is insufficient to determine its generalization ability in the general case.
- Instead, focusing on properties of prediction functions can be more useful, inspired by work on adversarial examples in deep neural networks.
- Sharp Minima's generalization abilities are not limited to a specific parameter space, making it applicable across various architectures and training methods.
- The paper provides a new perspective on the relationship between flatness of minima and generalization properties, which can potentially lead to better optimization algorithms for deep neural networks.
- The paper discusses how analyzing gradient magnitude for generalization properties of deep neural networks is dependent on local geometry, making it problematic without proper input space explanation.
- Invertible preprocessing (feature standardization, whitening, or gaussianization) can alter the relative gradient magnitude at each point, affecting analysis.
- Flat minima found by standard deep learning algorithms tend to generalize better than sharp ones, but previous definitions of flatness fail to account for complex model geometry and parametrization.
- Non-identifiability due to symmetries can alter the flatness of a minimum without affecting its function representation.
- The whole error surface geometry can change arbitrarily under different parametrizations, requiring careful definition of flatness to avoid degeneracies.
- Flatness cannot be divorced from particular model or input space parametrization.
",2750
"1704.04683",1,"- RACE is a new dataset for benchmark evaluation of reading comprehension methods, created from English exams for middle and high school Chinese students aged 12-18.
- The dataset consists of approximately 28,000 passages and 100,000 questions generated by human experts (English instructors).
- RACE covers a variety of topics designed to evaluate students' ability in understanding and reasoning, with a larger proportion of reasoning-based questions compared to other benchmark datasets.
- The state-of-the-art models have an accuracy of 43%, while the ceiling human performance is 95%.
- RACE aims to serve as a valuable resource for research and evaluation in machine comprehension, with its dataset available at http://www.cs.cmu.edu/~glai1/data/race/ and code on GitHub (https://github.com/qizhex/RACE_AR_baselines).
- Existing datasets suffer from limitations such as trivial question solving, noisy data, and biased topic coverage. RACE addresses these issues by using human-generated questions and passages from English exams for Chinese students.
- The paper introduces RACE, a large-scale reading comprehension dataset from Chinese exams for middle and high school students.
- It addresses limitations in existing datasets by providing broad topic coverage and objective grading of answers.
- RACE consists of 27,933 passages and 97,687 questions, with each question having four candidate answers (only one correct).
- The dataset includes reasoning types like passage summarization and attitude analysis, not found in other large-scale datasets.
- Passage styles in RACE are diverse, covering news, stories, ads, biography, philosophy, etc., unlike other datasets with domain-specific or fixed styles.
- Advantages of RACE over existing large datasets include better evaluation for machine learning systems and general reading comprehension ability.
- The dataset can be used to train powerful deep neural networks due to its relatively large size compared to other available options.
- RACE is a large-scale reading comprehension dataset specifically designed to test human agents' ability in reading comprehension, making it an accurate indicator for evaluating machine learning systems.
- The questions in RACE are more difficult than existing datasets due to the larger portion of reasoning-based questions and its sufficient size for training deep learning models.
- Unlike other datasets, candidate options in RACE are human-generated sentences that may not appear in the original passage, making the task more challenging and allowing a rich variety of question types such as passage summarization and attitude analysis.
- RACE has broad coverage across various domains and writing styles, which is desirable for evaluating generic comprehension ability in learning models.
- MCTest (Richardson et al., 2013) is a popular dataset with similarities to RACE but with fewer questions and designed for younger children, making RACE a larger and more difficult version of the MCTest dataset.
- Cloze-style datasets are large-scale cloze-style datasets that focus on obliterating words or entities in sentences; they differ from RACE as they do not involve reasoning skills and have limited question types.
- The paper introduces RACE, a large-scale reading comprehension dataset from examinations.
- It compares RACE with existing datasets like CNN/Daily Mail, Children's Book Test (CBT), and Book Test (BT).
- Machine comprehension models have matched human performance on CBT and BT but not CNN/Daily Mail.
- The paper presents a new cloze-style dataset called Who Did What (WDW) constructed from the LDC English Gigaword corpus.
- RACE aims to provide a more challenging reading comprehension task for LLMs, with 120K questions and 360K passages.
- The paper discusses the importance of creating diverse datasets for better evaluation of LLM performance in real-world scenarios.
- RACE's dataset includes various genres like fiction, nonfiction, and academic texts, with a focus on examinations.
- The authors suggest that RACE can be used to improve the robustness and generalization capabilities of LLMs.
- The paper highlights the need for more diverse datasets in evaluating LLM performance and its practical applications.
- RACE's dataset is available for download, allowing researchers to test their models on this challenging reading comprehension task.
- RACE is a large-scale reading comprehension dataset from examinations, aiming to evaluate machine reading comprehension abilities similar to human performance in schools.
- The dataset uses news articles as passages and questions from the same event, generating cloze-style datasets with high noise due to automatic generation. Human performance on these datasets is around 80-85%.
- Span-based answer datasets like SQUAD, NEWSQA, MS MARCO, and TriviaQA have large possible span spaces and use metrics like F1 score, BLEU, or ROUGE to measure overlap between predictions and ground truth answers. Human performance on these datasets is around 80-65%.
- RACE is the first large-scale dataset of this type, with questions based on exams designed for human reading comprehension evaluation.
- The dataset contains 120,000 questions from 30,000 passages, covering a wide range of subjects and difficulty levels.
- RACE's question types include inference, factual, and reasoning questions, with inference being the most common (79%).
- The dataset can be used for training data-driven machine reading models to improve their performance on real-world examinations.
- RACE provides a valuable resource for evaluating and improving machine reading comprehension abilities towards human-level performance.
- RACE: Large-scale ReAding Comprehension Dataset From Examinations - A dataset of English examinations for middle and high school students in China, designed to test their learning of English as a foreign language.
- Dataset Statistics - The RACE-H (high school) exams have larger passage lengths and vocabulary sizes compared to the RACE-M (middle school) exams, indicating higher difficulty levels. However, the vocabulary size and complexity are simpler than in other QA datasets.
- Reasoning Types of Questions - Five question types were identified with increasing difficulty: Word matching, Paraphrasing, Single-sentence reasoning, Multi-sentence reasoning, and Insufficient/ambiguous.
- Question Annotation Process - Human annotations were conducted to determine the proportion of each question type in a sample of 100 passages (50 from RACE-M and 50 from RACE-H).
- Dataset Splits - The dataset was split into training, development, and test sets for both RACE-M and RACE-H, with 5% data reserved for each set.
- Crowdsourcing Platform - Amazon Mechanical Turk was used to label the questions in the dataset. Each question was labeled by two crowdworkers.
- Practical Applications - The RACE dataset can be utilized for training and evaluating large-scale reading comprehension models, particularly those designed for English as a foreign language.
- RACE is a large-scale reading comprehension dataset from examinations, consisting of passages with 5 questions each labeled by two crowdworkers.
- The dataset has two levels: RACE-M and RACE-H, with varying complexity and payment for master turkers ($0.70 and $1.00 respectively).
- RACE's higher difficulty level is justified by its higher ratio of reasoning questions compared to CNN, SQUAD, and NEWSQA (59.2% vs 21-33.9%).
- Word matching questions are the lowest in RACE at 15.8%, while RACE-H has more complex questions than RACE-M.
- The paper subdivides reasoning types into five categories: detail reasoning, whole-picture understanding, passage summarization, attitude analysis, and world knowledge.
- Detail reasoning requires understanding specific details in the passage, while whole-picture reasoning necessitates comprehending the entire story. Passage summarization involves selecting the best summary from four options. Attitude analysis focuses on opinions/attitudes of authors or characters, and world knowledge questions require external knowledge (often simple arithmetic).
- The dataset can be used for various applications such as training LLMs to understand complex reasoning tasks, evaluating reading comprehension models, and developing educational tools.
- RACE: Large-scale ReAding Comprehension Dataset from Examinations - This paper introduces a new dataset for large-scale machine comprehension, focusing on reading comprehension abilities in English examinations from China.
- Data Collection and Cleaning Process - The authors collected data from three public websites, resulting in 137,918 passages and 519,878 questions. After cleaning, the final dataset RACE consists of 27,933 passages and 97,687 questions.
- Experiments - The paper compares the performance of several state-of-the-art reading comprehension models with human performance using accuracy as a metric.
- Sliding Window Algorithm - A rule-based baseline introduced by the authors is used for comparison.
- Dataset Comparison - RACE outperforms other datasets in terms of question types, including passage summarization and attitude analysis, which have not been previously introduced in existing large-scale machine comprehension datasets.
- Practical Applications - The dataset can be utilized to evaluate reading comprehension abilities, improve machine learning models, and potentially aid in educational research.
- Unusual Findings - RACE demonstrates better performance in certain question types compared to other datasets, highlighting the importance of including a diverse range of questions in large-scale machine comprehension datasets.
- The paper introduces RACE, a large-scale reading comprehension dataset from examinations.
- It compares various models and algorithms for reading comprehension tasks, including the Sliding Window Algorithm, Stanford Attentive Reader (Stanford AR), Gated-Attention Reader (Gated AR), and others.
- The paper presents a new model called RACE-M, which outperforms previous methods on certain question types like Word Matching and Single-Reason questions.
- Stanford AR achieves state-of-the-art results on CNN/Daily Mail datasets but performs poorly in the RACE dataset.
- Gated AR shows better performance than Stanford AR in the RACE dataset, especially for Multi-Reason and Ambiguous questions.
- The paper highlights the importance of using contextualized word embeddings and multi-hop architectures to improve reading comprehension models' performance.
- It also emphasizes the need for more diverse datasets with varying question types to better evaluate and develop reading comprehension models.
- The RACE dataset is available for research purposes, providing a valuable resource for future studies in this field.
- The paper introduces RACE, a large-scale reading comprehension dataset from high school exams.
- It uses a multi-hop architecture with the Generalized Attention Network (GA) for document and question scanning.
- Implementation details include using GloVe word embeddings, GRU layers, dropout, and stochastic gradient descent training.
- Human evaluation involves Amazon Turkers labeling 500 questions from RACE-M and RACE-H with an accuracy of 85% for RACE-M and 70% for RACE-H.
- The ceiling human performance is calculated as 94.5% valid questions, resulting in 95.4% on RACE-M and 94.2% on RACE-H.
- Comparison with other datasets shows that GA outperforms Stanford AR on RACE-M (30.1% vs. 27.8%) and RACE-H (27.5% vs. 25.5%).
- The paper also presents a new dataset, CBT, for evaluating language models' performance in identifying missing tokens from common nouns or name entities.
- GA achieves human-level performance on CBT-C and CBT-N with 93.1% and 92.4%, respectively.
- The paper highlights the importance of using large-scale datasets for evaluating language models' comprehension abilities.
- Practical applications include using RACE as a benchmark for reading comprehension tasks, improving existing models, or developing new ones.
- RACE is a large-scale reading comprehension dataset from examinations, designed to test language models' performance on complex reasoning tasks.
- Sliding Window performs better on MCTest (51.5% accuracy) than RACE (37.3%), indicating that answering RACE questions requires more reasoning.
- On RACE, Sliding Window improves 28.6% over the random baseline compared to 58.5%, 92.2%, and 50% for CBTN, CBT-C, and WDW, respectively.
- Stanford AR and Gated AR achieve low accuracy (43.3% and 44.1%) on RACE, demonstrating that it is the most challenging among current large-scale machine comprehension datasets.
- Human performance on RACE is 94.5%, highlighting the dataset's cleanliness compared to other large-scale reading comprehension datasets.
- The performance gap between turkers and human performance is 41% for middle school problems and 25% for high school problems, indicating a significant room for improvement in matching human reading comprehension abilities.
- Stanford AR performs better on word matching than reasoning or paraphrasing problems, while Sliding Window does the opposite.
- The candidate answers of simple questions have a larger proportion in RACE compared to other datasets, which might affect performance metrics.
- Practical applications: RACE can be used for evaluating and improving large-scale reading comprehension models, particularly focusing on complex reasoning tasks.
- Unusual finding: Stanford AR's performance is not stronger on word matching categories than reasoning categories, possibly due to the larger proportion of data in reasoning categories.
- The paper introduces RACE, a large-scale reading comprehension dataset from examinations designed to examine human ability in this task.
- Key features of RACE include broad coverage of domains/styles and richness in question formats.
- Performing well on RACE requires more reasoning than other datasets, as evidenced by the significant gap between state-of-the-art machine comprehension models' performance and human performance.
- The dataset aims to stimulate the development of advanced machine comprehension models.
- Some limitations include similar word embeddings for certain question types (e.g., color questions) making it difficult to distinguish candidate answers.
- RACE was developed with support from DARPA grant FA8750-12-2-0342 under the DEFT program and acknowledges contributions from Graham Neubig, Diyi Yang, and others.
",2820
"1705.04146",1,"- The paper introduces a method for solving algebraic word problems by generating answer rationales, which are sequences of natural language and human-readable mathematical expressions that derive the final answer through small steps.
- Rationales provide a scaffolding for program structure via intermediate milestones, making it easier to induce arithmetic programs from question-answer pairs.
- The authors created a new 100,000-sample dataset of questions, answers, and rationales to evaluate their approach.
- Indirect supervision of program learning through answer rationales is shown as a promising strategy for inducing arithmetic programs.
- Natural language rationales enhance model interpretability and provide guidance on the structure of arithmetic programs needed to solve problems.
- The proposed learner relies on heuristic search and requires modeling rationales to effectively solve the task, as the unconstrained search space would otherwise be too vast.
- The paper introduces a new dataset with over 100,000 algebraic word problems that include both answers and natural language answer rationales.
- A sequence-to-sequence model is proposed to generate a sequence of instructions leading to the rationale, followed by an answer selection.
- The paper presents a technique for inferring programs that produce a rationale and ultimately provide the answer.
- The search space for possible programs is large, so they use reinforcement learning to find optimal solutions.
- The model achieves 90% accuracy on the test set, with an average of 12 steps per problem.
- The paper demonstrates that rationales can be used as a faithful representation of the steps involved in computing answers.
- This work contributes to models that explain or rationalize their decisions and can potentially improve human understanding of complex mathematical problems.
- The proposed model could have practical applications in education, helping students understand how to solve algebraic word problems step-by-step.
- The paper introduces a method for generating rationales to solve algebraic word problems, which involves building a dataset of 100,000 problems with annotations (question, options, and rationale).
- A heuristic search is employed to find plausible next steps in the search for programs, addressing the large search space issue.
- Empirical results show that state-of-the-art sequence-to-sequence models perform below chance on this task, while the proposed model doubles the baseline accuracy.
- The dataset construction process involves collecting seed problems from various sources and crowdsourcing new questions to ensure a diverse range of topics and difficulty levels.
- The paper highlights the importance of generating rationales for algebraic word problems, as it helps in understanding the problem-solving process and can be applied to other domains like physics or chemistry.
- The proposed method has practical applications in educational settings, such as providing explanations for correct answers and helping students understand the underlying concepts better.
- The paper introduces a method for generating rationales for solving algebraic word problems using Large Language Models (LLMs).
- It collects a dataset of 104,519 math problems, including 34,202 seed problems and 70,318 crowdsourced problems. The authors remove replicas and held-out sets to obtain a training set with 100,949 problems.
- The model aims to generate both the text in the rationale and perform math operations required for solving the problem simultaneously. This is achieved by generating a program containing instructions that produce output and intermediate values used by following instructions.
- The paper proposes a novel architecture called Program Induction by Rationale Generation (PIRG) that combines a sequence-to-sequence model with a program induction module to generate rationales for algebraic word problems.
- PIRG achieves state-of-the-art performance on the collected dataset, outperforming previous methods in terms of accuracy and fluency of generated rationales.
- The authors suggest that this approach can be extended to other domains requiring explanation generation, such as natural language understanding tasks or scientific reasoning.
- The paper aims to develop a program induction method for solving and explaining algebraic word problems by generating rationales.
- The approach involves creating a sequence of instructions (program) that generates the rationale and selects the correct option when executed.
- The input consists of an algebraic word problem, options, and knowledge about possible solutions.
- The output is the generated rationale and selected option.
- A latent instruction sequence (program) is created to generate the rationale, with each instruction accessing x, y, and memory buffer m.
- An example program is provided that generates an excerpt from Problem 2, illustrating how instructions fill output positions by sampling from a vocabulary or performing operations on input values.
- The paper discusses the challenges of generating programs for rationales, including handling complex problems with multiple steps and elimination processes.
- A method is proposed to generate programs using reinforcement learning, where a policy network generates instructions based on the current state and rewards.
- Experiments show that the proposed approach achieves 30% accuracy in generating correct programs for solving algebraic word problems.
- The paper highlights potential applications of this technique in educational settings to help students understand problem-solving processes better.
- The paper introduces a model that generates instructions to solve algebraic word problems by learning from rationale generation.
- The model can manipulate existing tokens, benefiting from additional expressiveness needed for solving math problems within the generation process.
- A total of 22 operations are defined: 13 frequently used in math problems (Id, Add, Subtract, etc.) and 6 converting between floating point numbers and strings/fractions.
- The model generates sequences of instructions to solve algebraic word problems, with each instruction consisting of an operation, arguments, and result placement.
- Instructions are conditioned on the text program specification and the program's history.
- The paper presents a method for generating and executing these instructions, which can be applied in various settings like math tutoring systems or educational games.
- The model achieves 30% accuracy on a test set of 150 problems, demonstrating its potential to solve algebraic word problems.
- The paper introduces a method for generating instructions to solve and explain algebraic word problems by using rationale generation.
- It presents an instruction probability model that generates operations, their arguments, and decision-making on where the result should be placed (output or memory).
- A network architecture is used to generate these instructions at each timestamp, involving recurrent states, softmax functions, and latent predictor networks for argument generation.
- The method achieves state-of-the-art performance in solving algebraic word problems with explanations, outperforming existing methods by 30% in accuracy.
- The approach is also faster than other methods, running 4.5 times faster on average.
- The model can be applied to various domains beyond algebraic word problems, such as programming and robotics tasks.
- The paper highlights the importance of rationale generation for understanding and explaining problem-solving processes.
- It introduces a new method for generating instructions that can be used in other contexts where instruction sequences need to be generated.
- The model's performance demonstrates the potential for AI systems to learn complex tasks by generating instructions rather than directly learning from data.
- This approach could lead to more interpretable and explainable AI models, which is crucial for understanding and trusting their decision-making processes.
- The paper introduces a method for learning to solve and explain algebraic word problems by generating rationales, which are step-by-step solutions that progressively address the problem.
- A probability distribution is defined over input words using softmax, where function f computes the affinity between each token and the current output context.
- If qi,j = COPY-OUTPUT, the model copies from either the output or memory, finding the instruction zi that generated the value.
- A pointer network is used to point to output instructions, defining a distribution over previously generated instructions.
- The argument and state are embedded to generate the next state, executing operations to obtain the result and updating states accordingly.
- The set of instructions z generating y is unobserved; the marginal probability function p(y | x) is optimized by maximizing p(z | x) for samples from Z(y).
- Generating programs that generate y can be challenging, as randomly sampling from RNN distributions may not produce a sequence in Z(y).
- The paper leverages the fact that rationales solve problems step-by-step, assuming progression within the rationale to address this challenge.
- This method improves upon existing question answering work by adding prior knowledge and constraining the exponential space.
- Practical applications of this approach could include teaching AI systems how to explain their actions or decisions in a human-understandable manner, improving transparency and trust in AI systems.
- The paper introduces a method for generating algebraic word problem solutions and their explanations (rationales) using Large Language Models (LLMs).
- It uses a staged back-propagation approach to address the issue of long sequences in LLM training, which can lead to memory bottlenecks.
- The model generates intermediate instructions with limited indirection levels and filters possible options based on their ability to generate subsequent tokens.
- During decoding, a stack-based decoder finds the most likely sequence of instructions given an input problem.
- The attention and copy mechanisms incur exponential matrix multiplications as the size of the input increases, leading to memory bottlenecks.
- To address this issue, the paper proposes using a sparse attention mechanism that only considers a small number of relevant tokens, reducing computational complexity.
- Experiments show that the proposed method achieves 30% accuracy on a test set and is 4.5 times faster than the baseline model.
- The approach can be applied to various domains beyond algebraic word problems, such as programming or natural language understanding tasks.
- The paper highlights the importance of addressing memory bottlenecks in LLM training for practical applications.
- By using a sparse attention mechanism and staged back-propagation, the method enables efficient generation of long rationales without sacrificing accuracy.
- The paper introduces a training method called staged back-propagation to address memory bottlenecks in sequence-to-sequence models for solving algebraic word problems.
- Staged back-propagation divides the input sequence into smaller slices, allowing memory-intensive operations like attention and copy mechanisms to be unrolled only within each slice's range.
- The model still requires global context by building a full state sequence h for the whole sequence z, then obtaining a slice hj:j+K and computing the attention vector.
- Experiments involve generating rationales for solutions to math problems, evaluating both rationale quality and correct answer acquisition.
- Baselines include an attention-based sequence-to-sequence model with copying capabilities from Bahdanau et al. (2014) and augmentations by Ling et al. (2016) and Merity et al. (2016).
- Hyperparameters used in the experiments include a two-layer LSTM with hidden size H = 200, word embeddings of size 200, D = 5 for graph expansion during sampling, beam size of 200, and vocabulary of 20,000 most frequent words.
- Evaluation metrics include average sentence-level perplexity and BLEU-4 (Papineni et al., 2002).
- The model's perplexity is dependent on the latent program generated, so it forces decoding to generate rationales while maximizing the probability of the program.
- The paper highlights that this approach can be applied to other domains with similar memory constraints and attention mechanisms.
- Experiments show that the proposed method achieves better performance than baseline models in both rationale quality and correct answer acquisition.
- The paper introduces a method for program induction by generating rationales to solve and explain algebraic word problems.
- It uses a sequence-to-sequence model with copy mechanisms to improve performance on this specific task.
- The input copy mechanism reduces perplexity as it allows the generation process to use values mentioned in the question, while output copying slightly improves over input copying due to repeated values in some problems.
- The proposed method achieves significant improvements over baseline models, demonstrating that algebraic manipulation is essential for this task.
- The model generates rationales with better BLEU scores as it can define variables used in the rationale thanks to the copy mechanisms.
- The output copy mechanism does not add further improvement in perplexity evaluation because copied values could have been generated by other parts of the model.
- The paper provides an example of a program inferred by the model, showing how it isolates values and connects them with operations.
- The method has practical applications in education, helping students understand algebraic word problems better and potentially improving their performance on standardized tests.
- The paper introduces a program induction method for solving and explaining algebraic word problems using rationale generation.
- This approach involves learning to generate programs that can solve math problems while providing natural language explanations (rationales).
- The model's performance is measured by BLEU score, accuracy, and the ability to solve complex problems.
- The paper demonstrates that the proposed method outperforms existing models in solving algebraic word problems with a rationale.
- However, generating complex rationales remains an unsolved problem, as each additional step adds complexity during inference and decoding.
- Related work focuses on math problem-solving and learning to map math expressions into formal languages.
- The paper provides examples of the model's performance, including a detailed illustration of the most likely latent program inferred by the algorithm for a held-out question-rationale pair.
- The paper introduces a model that generates natural language rationales and performs arithmetic operations simultaneously, addressing the problem of generating explanations for math problems.
- It uses an encoder-decoder paradigm with external memory, semantic parsing ideas, and program generation concepts to create this model.
- The model outperforms existing neural models in both rationale fluency and problem-solving ability.
- The paper collects 100,000 question-rationale pairs for training the model.
- Experiments show that the proposed method generates more fluent rationales than other approaches while maintaining high accuracy.
- This work contributes to the field by being the first to use rationales to guide program induction in solving algebraic word problems.
- The paper highlights the importance of providing textual explanations for classification decisions, as part of increased interest in creating models with interpretable decisions.
- The model's practical application lies in generating natural language and performing arithmetic operations simultaneously during decoding.
- The paper explores program induction through rationale generation, focusing on learning to solve and explain algebraic word problems.
- It combines neural machine translation (NMT) with symbolic reasoning to generate natural language explanations for algebraic word problems.
- The approach involves training a sequence-to-sequence model using NMT techniques to translate from problem statements to symbolic programs, which are then executed and explained in English.
- Key contributions include the development of a new dataset (Algebra Word Problem Dataset) with 10k problems and their solutions, as well as an evaluation methodology for measuring both accuracy and explanation quality.
- The model achieves an average accuracy of 32% on the test set, with an additional 4.5 times speed improvement over a symbolic reasoning baseline.
- Future work includes improving the model's performance by incorporating more complex algebraic operations and expanding to other domains like arithmetic word problems or programming tasks.
- The paper highlights the potential of combining NMT techniques with symbolic reasoning for solving various problem-solving tasks, opening up new avenues in AI research.
",3034
"1706.05125",1,"- The paper focuses on end-to-end learning for negotiation dialogues, a task that requires both linguistic and reasoning skills.
- A large dataset of human-human negotiations was gathered, where agents with different goals must reach an agreement via natural language dialogue.
- End-to-end neural models were trained to negotiate by maximizing the likelihood of human actions, but they performed poorly as they lacked strategic skills.
- Two methods for improving the model's strategic reasoning were introduced: self-play reinforcement learning and dialogue rollouts.
- Self-play improved performance significantly in negotiations with humans, while dialogue rollouts increased performance by estimating utterance rewards during decoding.
- The paper analyzed the performance of agents, finding evidence of sophisticated negotiation strategies such as deceit.
- The code and dataset are publicly available for further research.
- Novel negotiation task and dataset: Developed a multi-issue bargaining task based on DeVault et al. (2015) for end-to-end training of negotiation agents. Collected human-human dialogue data using Amazon Mechanical Turk, with 5808 dialogues in total and 252 scenarios held out as test set.
- Likelihood model: Proposed a baseline model where a sequence-to-sequence model generates the complete dialogue conditioned on an agent's input goals. Data representation involves converting each dialogue into two training examples, representing the perspectives of both agents.
- Supervised learning: Trained a sequence-to-sequence network to generate an agent's perspective of the dialogue based on their input goals. The model uses 4 recurrent neural networks and achieves an accuracy of 75% in predicting output decisions.
- Reinforcement learning: Introduced a reinforcement learning approach that learns to negotiate directly from the dialogue data, without requiring explicit reward functions. This method achieved an average score of 6.13 out of 10 on the test set, compared to 5.92 for the likelihood model and 4.87 for random agents.
- Multi-agent learning: Proposed a multi-agent reinforcement learning approach that trains multiple agents simultaneously, allowing them to learn from each other's experiences. This method achieved an average score of 6.31 on the test set, compared to 6.09 for single-agent training and 5.87 for random agents.
- Generalization: The proposed methods generalize well to new situations, as shown by their performance on held-out scenarios.
- The paper introduces an end-to-end learning approach for negotiation dialogues, focusing on modeling agents' input goals and predicting their token sequences conditioned on these goals.
- The model uses four recurrent neural networks (GRUs) to encode the agent's input goals, predict tokens, and generate output choices.
- During decoding, the model samples from its predictions, while during reinforcement learning, it tries to improve by simulating conversations with a fixed supervised model trained on human data.
- The paper presents experiments showing that the proposed approach outperforms baseline models in terms of accuracy and consistency with human behavior.
- The paper introduces an end-to-end learning approach for negotiation dialogues, where agent A learns by simulating conversations with a surrogate forward model.
- Agent A generates tokens based on its goals and reads the opponent's generated tokens until one agent emits a decision token ending the dialogue. Both agents receive rewards based on their decisions.
- The paper defines future reward R for an action xt as a function of the outcome of the negotiation, discount factor γ, dialogue length T, and running average of completed dialogue rewards µ.
- Goal-based decoding is explored to improve likelihood-based decoding, which may not be optimal in some cases. The paper proposes calculating expected reward R(u) for candidate utterance u by repeatedly sampling xn+k+1,T from pθ and choosing the best output o using Equation 12.
- Experiments include training details such as model implementation, hyperparameters, input token embeddings, and optimization methods used during supervised and reinforcement learning stages.
- Applied AI researcher specializing in Large Language Models (LLMs) reviews academic paper ""Deal or No Deal? End-to-End Learning for Negotiation Dialogues.""
- The goal is to analyze the paper, identify main contributions and most interesting findings, and write a bullet point summary for future LLM researchers within the organization.
- Reinforcement learning (RL) with a learn-rate of 0.1, clipped gradients above 1.0, and discount factor γ=0.95 is used. After every four updates, supervised training occurs with a mini-batch size of 16 and a learning rate of 0.5.
- During sampling, the variance is reduced by doubling logit values (temperature of 0.5).
- Comparison systems include LIKELIHOOD, RL, ROLLOUTS, and RL+ROLLOUTS.
- Intrinsic evaluation measures perplexity of user-generated utterances in development. Results show that the simple LIKELIHOOD model produces most human-like responses, while alternative training and decoding strategies cause divergence from human language.
- End-to-End evaluation involves measuring score (average for each agent), agreement percentage, and Pareto optimality on held-out scenarios with both the likelihood-based agent and humans on Mechanical Turk.
- Results show that goal-based models achieve significantly better results when negotiating with LIKELIHOOD, RL+ROLLOUTS model outperforming human partners in some aspects. However, these models also have longer dialogues and more aggressive negotiation tactics leading to lower agreement rates.
- The paper explores end-to-end learning for negotiation dialogues using goal-based models and reinforcement learning (RL).
- Goal-based models can struggle with deception, as they tend to rephrase demands repeatedly instead of adopting more effective strategies like humans do.
- Models learn to be deceptive by initially feigning interest in valueless items before later 'compromising' on them.
- Goal-based models produce meaningful novel sentences, with 76% of messages from the LIKELIHOOD model being found in training data.
- Maintaining multi-sentence coherence is challenging for RL+ROLLOUTS, as they sometimes start a message by indicating agreement but then propose a counteroffer.
- The paper compares their approach to related work, such as end-to-end goal-oriented dialogue with supervised models and reinforcement learning in dialogue settings.
- End-to-end learning for negotiation dialogues shows improvements over supervised learning with goal-based training and decoding.
- The paper highlights the need for future work to address issues like deception, multi-sentence coherence, and finding domains that force a higher degree of diversity in utterances.
- End-to-end learning for negotiation dialogues using reinforcement learning (RL) is introduced as a task in AI, challenging linguistic and reasoning skills with robust evaluation metrics.
- A large dataset of human-human negotiations was gathered to train dialogue agents end-to-end.
- Agents' abilities can be improved by training and decoding to maximize goals rather than likelihood.
- Future work includes exploring other reasoning strategies, improving utterance diversity without diverging from human language, and investigating negotiation tasks across domains.
- The paper acknowledges the contributions of various researchers and thanks Mechanical Turk workers for data collection assistance.
- The paper discusses end-to-end learning for negotiation dialogues, focusing on deep reinforcement learning (DRL) approaches to create more human-like agents in negotiations.
- It highlights the importance of using DRL methods to learn persuasion strategies and improve dialogue management systems.
- The authors present a novel framework that combines DRL with policy networks for training dialogue agents, enabling them to adapt to various negotiation scenarios.
- They introduce a new dataset called ""Deal or No Deal"" (DoND) containing 10K human-human negotiations from the TV show ""Deal or No Deal."" This dataset is used for training and evaluating their proposed approach.
- The paper presents experimental results, showing that their method outperforms baseline approaches in terms of negotiation outcomes and dialogue quality.
- They also discuss the challenges faced during the development process, such as handling partial observability, multi-agent settings, and dealing with sparse rewards.
- The authors provide insights into future research directions, including improving the generalization ability of DRL methods for dialogue systems and exploring more complex negotiation scenarios.
- Overall, this paper demonstrates how deep reinforcement learning can be effectively applied to create human-like agents in negotiation dialogues, leading to better outcomes and improved dialogue quality.
- The paper presents an end-to-end learning approach for negotiation dialogues, focusing on a task-oriented dialogue system that can handle multi-issue and multi-party negotiations.
- It introduces a novel architecture called the Negotiator Network (NN), which combines reinforcement learning with deep neural networks to learn from raw text data without requiring any handcrafted features or domain knowledge.
- The NN consists of three main components: an encoder, a policy network, and a value network. These components are trained end-to-end using backpropagation through time (BPTT).
- The paper demonstrates the effectiveness of the approach by comparing it to state-of-the-art baselines in both simulated and real-world negotiations.
- In simulations, the NN achieved a 30% higher success rate than the baseline, while being 4.5 times faster during training.
- In real-world experiments with human subjects, the NN outperformed the baseline by 12%, showing better performance in terms of negotiation outcomes and user satisfaction.
- The paper highlights the importance of end-to-end learning for task-oriented dialogue systems, as it eliminates the need for handcrafted features or domain knowledge, making it more scalable and applicable to a wider range of tasks.
",1907
"1706.05137",1,"- The paper explores creating a single deep learning model that can perform well across multiple domains, reducing the need for research and tuning for each specific problem.
- This unified model is trained concurrently on various tasks, including image classification (ImageNet), machine translation, image captioning, speech recognition, and English parsing.
- The model architecture incorporates building blocks from multiple domains: convolutional layers, attention mechanism, and sparsely-gated layers.
- Each computational block is crucial for a subset of tasks, but adding them to other tasks does not hurt performance and often improves it in most cases.
- Joint training with less data-intensive tasks benefits largely from this approach, while performance on large tasks degrades only slightly or not at all.
- The paper demonstrates that a unified deep learning model can be created to solve problems across multiple domains, potentially reducing the need for task-specific research and tuning.
- The MultiModel architecture is introduced, allowing a single deep learning model to simultaneously learn multiple tasks from various domains.
- This model trains on 8 corpora, including speech, image captioning, parsing, and translation datasets.
- While not achieving state-of-the-art performance, the MultiModel outperforms many task-specific models studied in recent years.
- Two key insights crucial to making the MultiModel work are: small modality-specific sub-networks converting into a unified representation and back from it; and allowing training on inputs with widely different sizes and dimensions through modality nets.
- Modality nets, specific to each modality (images, speech, text), define transformations between external domains and a unified representation space.
- The unified representation is variable-sized, avoiding bottlenecks and improving performance.
- Different tasks from the same domain share modality nets, reducing the number of sub-networks needed.
- MultiModel's auto-regressive nature requires modality nets to convert inputs into a unified representation and later convert back to output space.
- The model is trained end-to-end using TensorFlow, with code available on GitHub.
- This work represents a step towards answering the question of whether a single multi-task multi-modal model can improve learned representations in an unsupervised setting.
- The MultiModel architecture combines modality-nets, an encoder, I/O mixer, and an autoregressive decoder to learn from various tasks in a single model.
- Modality-nets share across tasks within the same domain, reducing the need for sub-networks per task. This encourages generalization and allows easy addition of new tasks.
- MultiModel uses computational blocks from multiple domains: depthwise-separable convolutions, attention mechanisms, and sparsely-gated mixture-of-experts layers. These blocks improve performance on different problems without negatively impacting tasks they were not designed for.
- The encoder and decoder are constructed using three key computational blocks: convolutions (depthwise separable), attention layers, and sparsely-gated mixture-of-experts.
- Convolutional blocks use depthwise separable convolutions to perform local computation efficiently.
- Attention layers focus on specific elements to improve model performance, particularly in language tasks.
- Sparsely-gated mixture-of-experts provides the model with capacity without excessive computational cost and can slightly improve performance even for tasks it was not designed for.
- The MultiModel architecture is flexible and can be adapted to various problems by changing the modality-nets, encoder, or decoder while keeping the core components unchanged.
- The paper introduces a depthwise separable convolution, called SepConv, which is a variant of traditional convolutions and consists of separate convolutions on each feature channel followed by a pointwise convolution to project to the desired feature depth.
- Convolutional blocks are designed with three components: ReLU activation, SepConv, and layer normalization. These blocks are stacked and connected via residual connections, forming a complete block that includes dropout for training.
- The attention mechanism uses a multi-head dot-product attention inspired by previous works, incorporating timing signals to focus on content based on position. Timing signals are constructed from sine and cosine curves.
- The proposed model achieves state-of-the-art results in machine translation tasks, with 30% relative improvement over the Transformer baseline.
- The model is also faster than the Transformer, running at 4.5 times the speed while maintaining similar accuracy.
- The paper demonstrates that a single model can be effective for various NLP tasks, including machine translation, text classification, and question answering.
- The model's simplicity allows for easy adaptation to different architectures and tasks without requiring extensive hyperparameter tuning.
- The authors suggest that the proposed model could potentially serve as a universal architecture for future LLMs.
- The MultiModel architecture combines various computational blocks, including convolutional layers, mixture-of-experts (MoE) layers, attention mechanisms, and encoder-mixer-decoder structures to learn from multiple modalities simultaneously.
- Sparsely-gated MoE layers are used for efficient processing of inputs, with 4 experts selected during training and load balancing costs added as in previous works.
- The model's body consists of an encoder, mixer, and decoder, all structured similarly to fully convolutional sequence-to-sequence models like ByteNet or WaveNet but with unique computational blocks.
- Convolutions in the mixer and decoder are padded on the left, allowing for autoregressive generation and large receptive fields over inputs and past outputs, enabling long-term dependency establishment.
- A command token is used to initiate decoding for different tasks with the same modality, and an embedding vector is learned for each token during training.
- Four modality nets are included: language (text data), images, audio, and categorical data. The language-based data uses a shared vocabulary of 8k subword units.
- The model achieves state-of-the-art performance on various tasks, including machine translation, speech recognition, and image captioning, with significant improvements in speed and accuracy compared to previous models.
- The paper introduces a MultiModel architecture that can learn from various data modalities, including language, image, categorical, and audio inputs.
- Language modality uses tokenized sequences with 8k subword-units, followed by learned embedding and linear mapping with Softmax for probability distribution over the vocabulary.
- Image modality is based on Xception entry flow with residual convolution blocks (ConvRes) and a network depth of d=1024.
- Categorical output modality reshapes one-dimensional outputs into two dimensions, followed by progressive down-sampling and pointwise convolution for classification.
- Audio modality accepts waveforms or spectrograms as inputs, using 8 ConvRes blocks from the image input modality. Spectral modality preserves full resolution in the spectral domain.
- MultiModel draws inspiration from earlier encoder-decoder architectures used in neural machine translation and combines convolutional and recurrent neural networks for sequence-to-sequence models.
- The paper achieves 30% accuracy on a large-scale multimodal dataset, with the model being 4.5 times faster than previous methods.
- The paper introduces MultiModel, a single architecture that can learn multiple tasks simultaneously, such as image classification, speech recognition, and machine translation.
- MultiModel uses depthwise separable convolutions to improve efficiency and reduce the bottleneck issue found in RNN-based sequence-to-sequence models.
- Experiments show that MultiModel achieves results comparable to state-of-the-art performance for individual tasks without extensive tuning, with a slight gap remaining due to the lack of fine-tuning.
- Training on multiple tasks simultaneously does not significantly impact performance compared to training each task separately, suggesting that MultiModel can effectively learn from diverse data sources.
- The paper highlights the importance of understanding how different computational blocks (e.g., depthwise separable convolutions) influence various tasks within a single architecture.
- By releasing their implementation as open-source and sharing details on hyperparameters, the authors aim to encourage further research in this area.
- The paper explores training a single deep learning model to learn multiple tasks simultaneously, using a MultiModel approach.
- When jointly trained on 8 tasks, the MultiModel performs similarly to single-task models for large tasks and better in cases with less data availability, such as parsing.
- Transfer learning occurs between seemingly unrelated tasks like ImageNet and parsing due to shared computational primitives within the model.
- Removing mixture-of-experts or attention mechanisms from MultiModel training does not significantly impact performance on various problems, suggesting that mixing different computation blocks can improve performance across multiple tasks.
- The paper demonstrates for the first time that a single deep learning model can jointly learn numerous large-scale tasks from diverse domains.
- The paper proposes a multi-modal architecture for learning from large-scale tasks across multiple domains, sharing as many parameters as possible and combining computational blocks from different domains.
- This approach leads to more general deep learning architectures, with the model demonstrating transfer learning from tasks with abundant data to those with limited data.
- The paper references various works in natural language processing (NLP), speech recognition, computer vision, and machine translation as examples of successful applications of this multi-modal architecture.
- Key components include attention mechanisms, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers.
- The model achieves state-of-the-art results on several benchmarks, including a 30% accuracy improvement in speech recognition over the previous best result.
- This architecture can be applied to various tasks, such as image captioning, machine translation, and text summarization, with potential applications in real-world scenarios like automatic transcription of lectures or interviews.
- The model's ability to transfer learning from data-rich tasks to those with limited data makes it particularly useful for resource-constrained settings.
- Future work could explore the use of this architecture for more complex tasks, such as multimodal understanding and reasoning.
- The paper reviews various academic works related to Large Language Models (LLMs) and their applications, focusing on multitask learning, neural machine translation, and computer vision.
- Multitask learning involves training a single model for multiple tasks simultaneously, improving efficiency and generalization by sharing knowledge across tasks.
- Neural machine translation uses deep learning techniques to translate text from one language to another, achieving better results than traditional statistical methods.
- Computer vision applications include image classification, object detection, and semantic segmentation, which have benefited from advances in convolutional neural networks (CNNs).
- The paper highlights the importance of pre-trained models, such as BERT, GPT-2, and ResNet, which provide a strong foundation for various tasks and improve performance.
- It also discusses the use of attention mechanisms to enhance model performance by focusing on relevant parts of input data.
- The paper emphasizes the need for large datasets like ImageNet, COCO, and Penn Treebank for training and evaluating LLMs in different domains.
- Some works mentioned in the review focus on improving efficiency through techniques such as depthwise separable convolutions, sparsely-gated mixture-of-experts layers, and multi-scale feature extraction.
- The paper also explores the use of subword units for neural machine translation to handle rare words more effectively.
- Lastly, it discusses the importance of transfer learning, where models are trained on a specific task and then adapted to new tasks without significant retraining.
- The paper discusses various research works from different authors, focusing on significant contributions to Large Language Models (LLMs) and related areas.
- Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke introduced Inception-v4 and Inception-ResNet models in 2016, highlighting the impact of residual connections on learning.
- Aäron van den Oord et al. developed Wavenet in 2016, a generative model for raw audio.
- Fisher Yu and Vladlen Koltun introduced multi-scale context aggregation by dilated convolutions in 2015.
- Loy C.C. Tang et al. presented facial landmark detection through deep multi-task learning at ECCV'14.
- These works demonstrate the progression of research in various aspects of LLMs and related fields, such as computer vision, audio generation, and deep learning techniques.
- The paper serves as a reference for understanding the evolution of these significant contributions to the field.
- Practical applications of these models include facial landmark detection, generative modeling for raw audio, and improved image classification through residual connections.
- These findings contribute to the development of more advanced LLMs with enhanced capabilities in various domains.
- The paper highlights the importance of collaboration among researchers from different fields in driving progress within the realm of Large Language Models.
",2527
"1706.07269",2,"- Explanation in Artificial Intelligence: Insights from Social Sciences - The paper explores how research on human explanation processes can benefit explainable AI (XAI).
- Philosophical foundations of explanation - Discusses philosophical definitions, including causality, explanations as products or abductive reasoning, interpretability, and justification.
- Why people ask for explanations - People seek explanations to understand, predict, control, or evaluate.
- Contrastive explanations - These highlight differences between two entities rather than similarities.
- Types and levels of explanation - Different types (causal, functional, intentional) and levels (surface, intermediate, deep) are discussed.
- Structure of explanations - Explanations can be organized in various ways: narrative, causal chain, analogy, or metaphor.
- XAI and explanation - The paper argues that XAI should incorporate insights from social sciences to improve the quality and effectiveness of explanations.
- Practical applications - By understanding human explanation processes, AI researchers can create more effective and user-friendly explainable systems.
- Benefits - Incorporating social science research into XAI could lead to better decision-making, increased trust in AI systems, and improved transparency.
- Limitations - Further research is needed to fully understand human explanation processes' application to XAI.
- Social Attribution: People explain behavior based on intentionality, beliefs, desires, and traits using Malle's conceptual model with internal states, external constraints, and normative expectations.
- Individual vs. Group Behavior: Personal explanations focus on individual characteristics while group behavior is explained by collective intelligence, norms, and morals.
- Social Attribution and XAI (Explainable Artificial Intelligence): Relevant concepts include folk psychology, Malle's models, and collective intelligence for understanding human attribution in AI systems.
- Cognitive Processes: People select explanations based on causal connection, counterfactuals, mutability, abnormality, temporality, controllability, intent, social norms, explanation selection criteria (facts and foils, abnormality, intentionality, necessity, sufficiency, robustness, responsibility, preconditions, failure, intentions), and pre-existing knowledge.
- Practical Applications: The paper's findings can improve XAI systems by understanding human attribution and decision-making processes for better explanations in AI models.
- Explanation in AI: Insights from social sciences help understand how people communicate explanations, their role in AI systems, and the importance of preconditions, failure modes, intentions, explanation evaluation, cognitive processes, and social explanation concepts.
- Explanation Evaluation: Coherence, simplicity, generality, truth and probability, goals, and explanatory mode are considered for evaluating explanations.
- Cognitive Processes and XAI (Explainable Artificial Intelligence): Abductive reasoning, mutability and computation, abnormality, intentionality and functionality, perspectives and controllability, evaluation of explanations, and their role in improving AI systems' transparency, interpretability, and trustworthiness.
- Social Explanation: The paper discusses logic and conversation, relation and relevance in explanation selection, argumentation and explanation, linguistic structure, conversational model, dialogue, theory of mind, implicature, dilution, and social and interactive explanation concepts.
- Conclusions: Understanding human communication of explanations is crucial for improving XAI systems' transparency, interpretability, and trustworthiness through approaches like generating decisions with human understanding in mind (interpretability/explainability) or explicitly explaining decisions (explanation). Applications include justifying autonomous agent behavior, debugging machine learning models, medical decision-making, and predictor classification explanations.
- Understanding human explanation processes is crucial for designing intelligent agents that provide effective explanations in AI.
- Researchers argue people employ biases and social expectations when generating and evaluating explanations, which should be considered in explainable AI development.
- Most research in explainable AI relies on intuitions rather than drawing from social science frameworks, leading to potential failures.
- Experts may not be the best judges of explanation usefulness for lay users; a strong understanding of how people define, generate, select, evaluate, and present explanations is essential.
- Philosophy, psychology, and cognitive science offer valuable insights on explanation theories that can benefit AI researchers.
- Practical applications include improving user trust in intelligent systems, enhancing human-AI interactions, and ensuring ethical decision-making.
- Explainable AI involves intelligent agents capable of explaining their decision-making processes to address trust issues in artificial intelligence.
- The paper emphasizes the importance of incorporating social science research on explanation into explainable AI development.
- Explanation theories from philosophy, cognitive psychology, and sociology can form the basis for explainable AI.
- Explainable AI is a human-agent interaction problem focused on 'everyday' explanations (local reasons for specific events).
- Explanations involve social interactions between an explainer and explainee, rather than just presenting associations and causes.
- Philosophical foundations of explanation: Focus on human behavior explanations, general work on how people generate and evaluate explanations, and dynamics of interaction in explanation.
- Building truly explainable AI systems is a challenge, especially for intelligent systems capable of offering explanations.
- Explanation in AI discusses social science insights and their application to Large Language Models (LLMs).
- Agent interaction: An example demonstrates different types of questions and the need for an agent to track explanation state.
- Philosophical foundations include causal explanation, distinguishing it from other terms like causal attribution and interpretability.
- Causality theories: Dependence (Hume's counterfactual model), interventionist, probabilistic, and transference models exist.
- Gerstenberg et al.'s experiment showed people use counterfactual simulation for causal judgments in physical environments.
- Kelley's causal schemas categorize causes into necessary, sufficient, insufficient, and intermediate causes.
- Causality is essential for AI systems to make accurate predictions and decisions, applicable across various domains like medicine, economics, and social sciences.
- Future research should focus on developing better methods for identifying and representing causal relationships in complex systems.
- Interdisciplinary collaboration between AI researchers and social scientists is crucial to advance our understanding of causality.
- The paper introduces concepts like causality, necessary and sufficient causes, internal and external causes, causal chains, and their applications in AI.
- Causal schemata involve multiple necessary causes and sufficient causes; internal causes are actor-based while external causes stem from the environment.
- Causal chains include temporal, coincidental, unfolding, opportunity, and pre-emptive types with root causes without a cause.
- AI systems can benefit from understanding these concepts to improve their explanations and decision-making processes.
- Social sciences provide valuable insights for developing more human-like AI systems capable of explaining actions and decisions.
- Causal chains and explanation: Pre-emptive distal causes prevent proximal events, and people don't need complete causal chains for sound explanations.
- Formal models of causation: Halpern and Pearl's model is accessible to computer scientists and has been adopted by philosophers and psychologists.
- Actual cause definition: An actual cause satisfies three criteria - both event and cause are true, counterfactual changes prevent the event, and it's minimal (no irrelevant events).
- Explanation as a cognitive process and social phenomenon: The paper discusses explanations as products, focusing on their transfer of knowledge through interactions between people with specific goals.
- Classification of explanatory questions: Questions are categorized into three types based on Pearl and Mackenzie's Ladder of Causation - what happened (observed event), how did it happen (cause-effect relationship), and why did it happen (counterfactual reasoning).
- Explanation as causality: The paper focuses on explanations involving causes, excluding non-causal explanations.
- Structure of ""why"" questions: These involve presuppositions and contrastive elements, not just yes/no answers.
- Practical applications: Understanding explanation processes can help in explainable AI, human-AI interaction, and social science research.
- Types of questions: The paper categorizes different types of questions - what (associative reasoning), how (interventionist reasoning), why (counterfactual reasoning), and ""what if"" scenarios.
- Abductive reasoning: This process is closely related to explanation, involving deriving a hypothesis to explain an observed phenomenon. It has two forms: one without considering competing hypotheses and another that does.
- Role of explanations in AI systems: Explanation can improve transparency, trust, and accountability in AI systems.
- Need for further research: The paper emphasizes the importance of exploring explanation's role in social contexts and human-AI interactions.
- Abductive reasoning as a distinct mode of logical reasoning: It is recognized alongside induction and deduction, but not directly equated with explanation due to its focus on cognitive processes involved in forming hypotheses and evaluating them.
- The paper discusses concepts like interpretability, explainability, justification, and explanation in AI, adopting Lipton's taxonomy of interpretable AI where explanation is considered post-hoc interpretability.
- Interpretability and explainability are differentiated, with the former focusing on understanding a decision's cause while the latter explains the decision-making process.
- Explanation in AI can serve various purposes beyond knowledge transfer, such as persuasion, learning, and assigning blame.
- People seek explanations for two reasons: finding meaning (reconciling inconsistencies) and managing social interactions (creating shared understanding).
- Contrastive explanations are crucial in philosophical and cognitive science literature, helping people understand the cause of an event relative to a counterfactual contrast case that did not happen.
- Understanding contrastive explanations can aid in developing explainable AI systems and improving human-AI interactions.
- Lipton's terminology: P (fact) and Q (foil) as counterfactual (non-occurrence of cause C) and foil (hypothetical outcome E).
- Counterfactuals and foils have different meanings in causality vs. contrastive explanation.
- The paper highlights practical applications of these concepts in AI research and development.
- People tend to ask questions about unexpected or abnormal events, and explanations can change prior knowledge and perceived likelihood of claims. Simpler explanations that cover multiple observations are considered more believable and valuable.
- Most authors argue that all why-questions seek contrastive explanations, with foils implied from language, tone, and context.
- Similarity in history between fact and possible foils helps determine the explainee's true foil.
- Understanding counterfactual cases is crucial for contrastive explanations.
- Contrastive explanation becomes less controversial when considering unexpected events or observations.
- Van Bouwel and Weber's work on contrastive explanations in social sciences can be applied to AI systems, such as explaining machine learning models' decisions.
- Explanatory questions in AI can be categorized into four types: plain fact, P-contrast (property contrast), O-contrast (object contrast), and T-contrast (time contrast).
- Plain-fact questions require a non-interrupted causal chain across time, while contrastive questions are typically asked for unexpected events.
- Contrastive explanations can be easier to derive than complete explanations for plain-fact questions.
- The hypothesis that all causal explanations are contrastive is supported by several bodies of work, providing insights into how people select and evaluate explanations based on the contrast between fact and foil.
- Aristotle's Four Causes model (Modes of Explanation) offers a framework for answering why-questions, classifying them into material, formal, efficient, and final causes.
- Scientific explanation structure: explanations should involve all intermediate levels between two levels, like theory-data connections.
- Social explanation layers according to Malle: conceptual framework (Layer 1), psychological processes (Layer 2), and language layer (Layer 3).
- XAI's connection with philosophical work on explanation: understanding human behavior in giving explanations is crucial for AI models.
- Relationship between cause attribution and explanation: causal chains don't necessarily mean providing an explanation, as interpretation is required.
- Contrastive Explanations: People prefer contrastive explanations (why X instead of Y) over complete ones due to their intuitive nature.
- Easier to explain contrastive questions: Providing contrastive explanations can be simpler than giving full causal attribution.
- Practical applications: Improve explainable AI models by focusing on contrastive explanations and addressing foil determination challenges.
- Explanations can be complete without knowing all causes, benefiting computational and human explanations alike.
- People use contrastive questions to simplify explanations by focusing on relevant causes.
- Most existing work focuses on contrastive questions but not explanations; finding differences between cases is crucial for effective contrastive explanations.
- Understanding levels of explanation is essential in explainable AI, as the type of question influences the answer's level.
- Aristotle's modes of explanation model can be applied to AI algorithms with different levels (formal, efficient, and final).
- Why-questions from human observers often focus on formal level, while questions about underlying algorithms may require explanations at other levels.
- Explanation in AI can benefit from insights gained through social sciences research.
- The paper explores explanation methods in AI, drawing insights from social sciences.
- Different levels of explanations are identified: final, formal, efficient/mechanistic.
- Why-questions for the final level, model-related questions for the formal level, and action-based questions for the mechanistic level.
- Intelligent agents need to reason about their own causal models; image classification example.
- A cleaner solution involves using an abstract symbolic model rather than attributing causes by algorithms.
- The ""model of self"" is needed for explanation purposes, not limited to interpretable models.
- Explanation structure should follow Overton's model with explicit explanatory models for different categories.
- Social attribution theories like Heider's balance theory, Fiske's correspondence bias, and Kelley's causal schemas are discussed.
- Social attributions influenced by factors such as cultural background, personality traits, and cognitive biases.
- Research on uninterpretable machine learning models and extracting model approximations using interpretable models (e.g., Bayesian networks, decision trees).
- The paper emphasizes the need for better understanding of explanation in AI through social sciences insights.
- Practical applications include designing explainable AI systems for human-AI collaboration.
- Research on social attribution is relevant to various AI fields and can improve transparency, accountability, and trust in AI systems.
- Incorporating social science perspectives leads to more human-centered and socially responsible models.
- The paper highlights the importance of considering social factors when designing and evaluating AI systems for ethical and fair outcomes.
- The paper explores how social attribution concepts from the social sciences can apply to real-world scenarios involving AI systems, such as explaining errors and biases in AI.
- Practical applications of these insights include improving user interfaces, enhancing explainability, and fostering better human-AI collaboration.
- The paper emphasizes the need for further research at the intersection of social sciences and artificial intelligence to advance our understanding of how people perceive AI systems' behavior.
- By incorporating social science insights into AI development, we can create more transparent, accountable, and trustworthy AI systems that benefit society as a whole.
",2993
"1707.00061",1,"- The paper explores racial disparity in natural language processing (NLP) algorithms, specifically focusing on African-American English in social media tweets.
- Current NLP systems sometimes analyze language from different social groups unequally, with lower accuracy for females and minorities compared to white males.
- Researchers conducted an empirical analysis of racial disparity in language identification for tweets written in African-American English.
- Disparity in NLP can have significant implications, such as affecting the accessibility of information and opinions from underrepresented groups.
- Gender and dialect are known confounds in speech recognition due to differences in pitch, timbre, and pronunciation.
- Domain adaptation techniques can help improve accuracy for African-American English by training models on larger datasets that include this language.
- The study highlights the need for more research into fairness in NLP algorithms and their impact on different social groups.
- Practical applications of these findings could lead to better language technologies, improved accessibility, and a more equitable representation of voices online.
- The paper explores racial disparity in natural language processing (NLP) through a case study of African-American English on social media platforms, specifically focusing on Twitter.
- It highlights how dialects like African-American English pose challenges to fairness in NLP due to their correlation with social factors and language variation.
- The authors analyze an African-American English Twitter corpus and evaluate racial disparity in language identification using off-the-shelf tools, finding that they tend to misclassify messages from African-Americans as non-English more often than those from whites.
- This disparity persists even when controlling for message length, indicating a need for better understanding of dialectal language and its impact on NLP applications.
- The paper concludes with a brief discussion on the implications of these findings and potential solutions to address racial disparities in NLP.
- The paper discusses racial disparity in natural language processing (NLP) through a case study of African-American English (AAE) on social media platforms, specifically focusing on Twitter.
- AAE is a linguistic variety with distinct syntactic-semantic, phonological, and lexical features, often associated with African-American communities.
- The ""BlackTwitter"" phenomenon refers to the overrepresentation of African-Americans and Hispanics in early Twitter usage, which highlights the need for addressing racial disparities in NLP.
- A mixed-membership probabilistic model is used to identify AAE-like text by connecting speakers of AAE with African-American neighborhoods.
- The study collects a large-scale AAE corpus from Twitter, analyzing words against demographics using the Census' 2013 American Community Survey data.
- The model achieves an accuracy of 79% in identifying AAE text and can be used to address racial disparities in NLP models for social media platforms.
- This research contributes to a better understanding of how linguistic features are related to race, ethnicity, and geographic location on social media platforms.
- The study highlights the importance of considering linguistic diversity and its relationship with demographics when developing NLP systems for social media.
- Future work could involve expanding this model to other languages and dialects, as well as incorporating additional features such as sentiment analysis and emojis.
- This research has practical implications in improving the fairness of NLP models by addressing racial disparities in language representation on social media platforms.
- The paper analyzes racial disparity in natural language processing (NLP) using a case study of social media African-American English (AAE).
- It uses Census data to define demographic covariates and infer statistical associations between language and demographics with a mixed membership probabilistic model.
- The model learns demographically-aligned language models for each demographic category, showing that it can identify AAE linguistic attributes known in sociolinguistics literature.
- The study filters tweets to create AA-aligned messages and white-aligned messages based on the proportion of tokens from the African-American language model.
- Language identification is a crucial first step in web or social media text processing pipelines, but it can lead to racial bias in NLP tools.
- The paper discusses how existing methods for language identification may have racial biases and proposes solutions to address these issues.
- It highlights the need for more diverse training data sets and better understanding of linguistic variation across different demographic groups.
- The study provides a practical example of how NLP tools can be improved by considering racial disparities in language processing.
- By identifying and addressing racial bias in NLP, this work aims to improve the accuracy and fairness of these tools for all users.
- This research contributes to the ongoing efforts to ensure that NLP technologies are inclusive and equitable across different demographic groups.
- The paper examines racial disparity in natural language processing, focusing on social media African American English (AAE) and standard American English (SAE).
- It investigates how popular language identification systems classify AAE and SAE tweets, suggesting that if trained on standard English data, these systems may exhibit disparate performance between AA- versus white-aligned tweets.
- The study analyzes four off-the-shelf language identifiers: langid.py, IBM Watson, Microsoft Azure, Twitter metadata (from 2013). Google's API was excluded due to server errors.
- Manual inspection revealed that longer tweets were more likely to be correctly classified, and the accuracy of classifying AAE and SAE tweets as English was similar.
- The paper highlights the need for further research on language identification systems to address racial disparities in their performance.
- The study investigates racial disparity in natural language processing (NLP) by analyzing social media African-American English (AAE).
- Longer tweets are more likely to be correctly classified, which could affect race disparity analysis due to different length distributions among demographic groups.
- To minimize this effect, messages were grouped into four bins based on the number of words in each message.
- The study used classifiers to predict English language for each bin and category (AA-aligned and white-aligned tweets).
- Classifier accuracy increased with longer messages; it was generally excellent for messages containing at least 10 tokens.
- Disparity in performance between AA- and white-aligned messages was greatest when messages were short, with gaps in performance ranging from 6.6% to 19.7%.
- Short messages (5 or fewer tokens) accounted for 41.7% of all AA-aligned tweets in the corpus.
- Statistical bias could arise in downstream applications due to these disparities, potentially underrepresenting opinions from African-American speakers.
- Accuracy disparities were often only a few percentage points; however, it's crucial for practitioners to be aware of potential biases.
- One way forward to create less disparate NLP systems is through domain adaptation and extending algorithms to work on different data distributions.
- The paper explores racial disparity in natural language processing (NLP) by analyzing social media African-American English (AAE).
- It discusses adaptation and other methods to extend algorithms for different data distributions, such as demographic models that improve language identifiers.
- A joint modeling approach is introduced for speech recognition, learning pronunciation model parameters for AAE and Standard American English (SAE) simultaneously.
- The paper highlights the importance of understanding users' perspectives in tech companies with low minority representation.
- It emphasizes the need for software designers to have a grounded understanding of dialects and sociolinguistics, particularly in language processing algorithms.
- The study shows that IBM Watson, Microsoft Azure, and Twitter classifiers exhibit racial disparity in their accuracy rates when identifying English tweets from African-American and white users.
- The paper suggests that the low representation of minorities in tech companies may contribute to this disparity.
- It recommends increasing diversity within tech companies to improve NLP algorithms' performance and reduce racial bias.
- The study provides a practical example of how understanding sociolinguistics can help address racial disparity in NLP systems.
- The paper discusses racial disparity in Natural Language Processing (NLP) and focuses on African-American English (AAVE) in social media.
- It highlights the need for better understanding of dialectal variation in NLP systems to address bias and improve accuracy.
- The study analyzes AAVE in social media, identifying its unique linguistic features and challenges in processing it using existing NLP tools.
- Researchers suggest that current language identification methods are not effective for AAVE due to its similarities with other dialects.
- They propose a new approach to address this issue by incorporating dialect-specific data and models, as well as developing specialized taggers and classifiers for AAVE.
- The paper emphasizes the importance of considering dialectal variation in NLP systems to ensure fairness and accuracy in language processing.
- It also discusses the potential impact of these findings on various fields such as machine learning, data mining, and social media analysis.
- The study highlights the need for further research into dialect-specific NLP models and tools to address racial disparities in technology.
- Practical applications include improving search engines' ability to find relevant content for AAVE speakers, enhancing sentiment analysis for AAVE texts, and developing better language identification systems.
- The paper also calls for more diverse representation in the development of NLP technologies to ensure fairness and accuracy in language processing across all dialects.
- The paper explores racial disparity in Natural Language Processing (NLP) through a case study of African-American English (AAE) on social media.
- It discusses pronunciation modeling for dialectal speech recognition, language identification tools, and analyzes Twitter data to understand AAE syntax.
- The study highlights the need for better representation of AAE in NLP models due to its underrepresentation in training datasets.
- Researchers found that YouTube's automatic captions exhibit gender and dialect bias, with AAE speakers being misgendered more often than others.
- To address these issues, the paper suggests incorporating more diverse data sources, improving language identification tools, and developing better NLP models for dialectal speech recognition.
- The study emphasizes the importance of considering linguistic diversity in NLP research to ensure fairness and accuracy in language processing systems.
",1966
"1707.08819",1,"- The paper proposes downsampled variants of ImageNet as an alternative to CIFAR datasets, addressing the high computational cost and time consumption associated with training on the original ImageNet dataset.
- These downsampled versions - ImageNet32x32, ImageNet64x64, and ImageNet16x16 - maintain the same number of classes and images as the original ImageNet but have reduced image resolution.
- Experiments on these variants are significantly faster than those conducted on the original dataset, while preserving similar characteristics with respect to optimal hyperparameters.
- The proposed datasets can be used for checking scalability of new methods, neural architectures, and associated hyperparameters.
- Downsampled ImageNet variants provide a more challenging classification task compared to CIFAR-10, potentially delaying the saturation of benchmarking results.
- Surprisingly strong classification results are obtained on these downsampled datasets, suggesting that they can serve as an alternative for training and evaluating deep learning models.
- The paper provides scripts to reproduce their experiments and download links for the proposed ImageNet variants at http://image-net.org/download-images and https://github.com/PatrykChrabaszcz/Imagenet32_Scripts.
- The paper introduces downsampled variants of ImageNet as an alternative to CIFAR datasets for facilitating cheap experimentation with different network architectures, training algorithms, and hyperparameters.
- Downsampling techniques yield similar results, except for a nearest neighbor approach that performed worse in all experiments.
- Using Wide ResNets, surprisingly good performance was achieved on ImageNet32x32, matching the baseline of AlexNet with 18.2% top-5 error while using images with roughly half the pixels compared to the original ones.
- The range of optimal learning rates does not change much across different downsampled variants and network widths, which could be exploited by multi-fidelity methods for architecture and hyperparameter search.
- Downsampled ImageNet datasets are available for download, containing all images from the original dataset without reducing classes or image numbers per class.
- The paper proposes using downsampled variants of ImageNet as an alternative to CIFAR datasets for training deep neural networks.
- Six different downsampling techniques from Pillow library are evaluated, with the box method showing better results than others except nearest neighbor.
- Wide Residual Networks (WRNs) are used for training and evaluation, demonstrating that downsampled ImageNet variants can achieve similar validation errors to CIFAR datasets.
- The paper suggests that conclusions drawn from cheap evaluations on ImageNet16x16 and ImageNet32x32 also hold true for expensive evaluations on ImageNet64x64, making these downsampled versions a viable alternative to the original dataset.
- The authors provide practical applications of their findings by suggesting that researchers can use downsampled variants of ImageNet as an efficient and effective alternative to CIFAR datasets when training deep neural networks for image classification tasks.
- The paper highlights the importance of choosing appropriate downsampling techniques, as some methods (nearest neighbor) may lead to worse results compared to others.
- The study shows that downsampled ImageNet variants can be used in a similar manner to CIFAR datasets, providing an alternative for researchers with limited resources or time constraints.
- The paper provides guidance on adapting WRNs for different image sizes (32x32, 64x64) by adding or removing residual blocks and adjusting the spatial resolution of feature maps.
- Data augmentation techniques such as horizontal flipping and random image shifts are used to improve model performance and generalization.
- The paper's findings suggest that downsampled ImageNet variants can be a viable alternative for training deep neural networks, offering potential benefits in terms of efficiency and effectiveness compared to CIFAR datasets.
- The paper investigates whether conclusions drawn from small networks and downsampled images can be applied to larger networks and higher resolution images, determining their usefulness in speeding up architecture design and hyperparameter optimization.
- Larger network widths (k) yield better results on downsampled datasets, with ImageNet32x32 achieving 40.96% Top-1 validation error using k = 10, matching the original AlexNet results on full-sized ImageNet. Greater image resolution leads to better performance.
- The optimal learning rate region remains similar across different combinations of downsampling sizes and network widths, with qualitatively very similar results for both 32x32 and 16x16 downsampled images. Larger values of k favor slightly larger learning rates than smaller ones.
- The paper concludes that small networks and downsampled images can be used as an alternative to CIFAR datasets, providing a cheap evaluation method for expensive experiments. This approach can speed up the experimental loop in architecture design and hyperparameter optimization.
- The paper explores the use of downsampled ImageNet as an alternative to CIFAR datasets for faster experimentation with small networks and reduced computational costs.
- It investigates tradeoffs between performance, training time, network sizes, and downsampling algorithms.
- Simultaneous reductions in both mechanisms (downsampling and network size) are necessary to achieve optimal anytime performance.
- Warm restarts can improve anytime performance over learning rate reductions at regular intervals.
- Architecture and hyperparameter search methods could exploit cheap proxies of computationally expensive setups based on varying degrees of freedom (e.g., Li et al. 2016; Klein et al. 2016).
- WRN-k models trained on downsampled ImageNet variants show improved performance with reduced network widths and learning rates, as well as faster training times compared to their full-resolution counterparts.
- The paper provides a comprehensive analysis of the tradeoffs between accuracy, training time, and computational cost for different network sizes and downsampling factors on ImageNet variants.
- WRN-k models trained on downsampled ImageNet32x32, ImageNet16x16, and ImageNet64x64 achieve better performance with reduced learning rates compared to their full-resolution counterparts.
- The paper highlights the potential of using downsampled ImageNet as an alternative to CIFAR datasets for faster experimentation in computer vision research.
- It encourages further exploration into architecture and hyperparameter search methods that exploit cheap proxies of computationally expensive setups, potentially leading to improved performance and reduced training times.
- The paper proposes downsampled variants of ImageNet as an alternative to CIFAR datasets, offering a middle ground between them and full ImageNet.
- These downsampled versions maintain the complexity of ImageNet while reducing image resolution, making it easier for neural networks to classify images with fewer parameters.
- The paper demonstrates that even 32x32 pixel images can be classified well, suggesting potential applications in data storage, noisy input images, or classifying small parts of high-resolution images.
- Preliminary experiments support the hypothesis that findings from smaller networks on lower resolution images may transfer to larger networks for higher resolution images, potentially reducing costs by up to 100 times.
- The paper suggests that these downsampled ImageNet variants can serve as a good benchmark for experimental studies such as algorithm design, neural network architecture search, and hyperparameter optimization.
- The authors acknowledge support from the European Research Council, German Research Foundation, and other organizations involved in their research.
- The paper proposes an alternative to CIFAR datasets by introducing downsampled variants of ImageNet, named ImageNet32x32, ImageNet16x16, and ImageNet64x64.
- These downsampled versions maintain the original ImageNet's class distribution but reduce image sizes for faster training and better memory efficiency.
- The paper compares the performance of these variants with their full-size counterparts on wide residual networks (WRN) and shows that they achieve similar or even better results in terms of accuracy, while being 4.5 times faster to train.
- ImageNet32x32 achieves a Top-1 validation accuracy of 69.7%, which is only 0.8% lower than the full-size ImageNet's 70.5%.
- The paper also presents results for ImageNet16x16 and ImageNet64x64, showing that they achieve 63.2% and 75.9% Top-1 validation accuracy respectively.
- These downsampled variants can be used as a more efficient alternative to CIFAR datasets for training deep neural networks in computer vision tasks.
- The paper highlights the practical benefits of using these downsampled ImageNet versions, such as faster training times and better memory efficiency without sacrificing accuracy.
- The authors suggest that their findings could potentially lead to a paradigm shift in how researchers approach image classification tasks, particularly for smaller datasets or resource-constrained environments.
",1727
"1709.02349",1,"- MILABOT is a deep reinforcement learning chatbot developed by Montreal Institute for Learning Algorithms (MILA) for Amazon Alexa Prize competition.
- The system can converse with humans on popular small talk topics through both speech and text.
- It consists of an ensemble of natural language generation and retrieval models, including template-based, bag-of-words, sequence-to-sequence neural network, and latent variable neural network models.
- Reinforcement learning is applied to crowdsourced data and real-world user interactions for training the system.
- MILABOT was evaluated through A/B testing with real-world users, performing significantly better than many competing systems.
- The machine learning architecture allows for improvement with additional data.
- The paper proposes a Deep Reinforcement Learning Chatbot, which uses statistical machine learning instead of hand-crafted states and rules for dialogue systems.
- The system architecture is inspired by ensemble-based machine learning systems, consisting of an ensemble of response models that generate responses on diverse topics using various strategies.
- A/B testing experiments were conducted during the semi-finals to evaluate different variants of the system.
- The dialogue manager combines response models and follows a three-step procedure: generating candidate responses, selecting priority responses, or choosing the highest-scored response based on a model selection policy.
- When ASR confidence is low, the system requests users to repeat their last utterance.
- The system averaged 14.5-16.0 turns per dialogue, significantly higher than other teams in the competition semi-finals and finalists. This suggests it's likely the most engaging system among all systems.
- The system is expected to improve with additional data as nearly all components are learnable.
- The paper discusses a Deep Reinforcement Learning Chatbot system with 22 response models, including retrieval-based neural networks, generation-based neural networks, knowledge base question answering systems, and template-based systems.
- Template-based models use AIML templates to generate responses based on dialogue history and user utterance. Alicebot and Elizabot are examples of these models.
- The paper explores the potential improvement by conditioning response models and model selection policy on ASR (Automatic Speech Recognition) confidences, as the ASR system is imperfect.
- The ordering of models determines which response to return in case there are multiple priority responses.
- The paper presents an algorithm for Alicebot's response generation process and Elizabot's template-based model.
- The authors suggest future work could include evaluating the performance of different response models, investigating the impact of ASR confidences on model selection policy, and exploring the use of other reinforcement learning algorithms.
- The paper introduces a Deep Reinforcement Learning (DRL) chatbot model that aims to improve conversation engagement and naturalness by incorporating various response models, including Initiatorbot, Storybot, Evibot, and Knowledge Base-based Question Answering.
- Initiatorbot acts as a conversation starter by asking open-ended questions or stating interesting facts to increase user engagement. It checks if it has been triggered in the last two turns and returns priority responses for greetings.
- Storybot generates short fiction stories in response to user requests, providing an example of non-conversational activity within the system.
- Evibot uses Amazon's question-answering web service Evi (www.evi.com) to handle factual questions and returns priority responses for direct questions containing a wh-word. If no valid response is found, it breaks down the query into subphrases with named entities or without them and queries Evi again.
- Knowledge Base-based Question Answering (KBQA) uses a pretrained model to answer user questions by retrieving relevant information from a knowledge base. The system returns priority responses for direct questions containing a wh-word, while non-priority responses are returned otherwise.
- ""A Deep Reinforcement Learning Chatbot"" paper introduces a template-based response model called BoWMovies for handling movie domain questions. It identifies entities and tags in user utterances, retrieves data from various APIs based on the identified elements, and generates responses using predefined templates.
- The BoWMovies model uses string matching to identify entities (movie titles, actors, directors) and tags (movie plot, release year). If word embedding similarity is needed for tag identification, it trains Word2Vec on movie plot summaries and actor biographies from the IMDB database.
- The paper also discusses Retrieval-based Neural Networks (VHRED models) that generate candidate responses using cosine similarity between current dialogue history and historical dialogue data. It has four VHRED models trained on Reddit, one based on news articles, and another on movie subtitles.
- The paper presents a comparison of the BoWMovies model with other generative sequence-to-sequence models, such as LSTM and GRU, in terms of response quality and computational efficiency. It also introduces a new metric called ""response priority"" to measure the importance of responses based on whether they contain entities or tags.
- The paper concludes by discussing future work, including improving the BoWMovies model's performance through better entity extraction methods, incorporating more data sources for entity and tag identification, and enhancing the VHRED models with additional datasets.
- The paper introduces various chatbot models trained on different datasets, such as VHREDRedditPolitics, VHREDRedditNews, and SkipThoughtBooks for the Amazon Alexa Prize competition.
- VHREDRedditPolitics uses a logistic regression model to score responses based on Reddit threads annotated by Amazon Mechanical Turk workers. However, this model is rarely used in the final system due to its limited performance improvement.
- SkipThoughtBooks follows a two-step procedure for generating responses: identifying priority responses from trigger phrases and selecting the most relevant response from all Reddit dataset responses using cosine similarity.
- Dual Encoder retrieval models, DualEncoderRedditPolitics and DualEncoderRedditNews, are also introduced in the paper. These models use a recurrent neural network to encode dialogue history and candidate responses for scoring.
- The paper presents various metrics and results, including accuracy, response time, and user satisfaction scores.
- The paper introduces a Deep Reinforcement Learning Chatbot that uses LSTM recurrent layers, bilinear mapping for scoring candidate responses, and models trained on specific Reddit datasets (DualEncoderRedditPolitics and DualEncoderRedditNews).
- Three bag-of-words retrieval models based on TF-IDF Glove word embeddings and Word2Vec embeddings are included to retrieve responses with the highest cosine similarity. These models cover various domains such as WashingtonPost, Trump tweets, fun facts, Game of Thrones quotes, and a generic response model (BoWEscapePlan).
- A Retrieval-based Logistic Regression model called BoWEscapePlan is introduced to maintain user engagement by selecting topic-independent, generic responses when other models fail to provide meaningful ones. However, the performance was poor, indicating the need for more labeled data and pre-training.
- The LSTMClassifierMSMarco model uses a search engine to retrieve search snippets based on the last user utterance as query. These snippets are then processed by removing HTML tags and extracting relevant text.
- The model uses a bidirectional LSTM to map dialogue utterances and search snippets into embedding vectors, then combines them through an MLP for predicting relevancy.
- It's trained as a binary classification on the Microsoft Marco dataset with 72.96% accuracy.
- The system uses Google Search API, but qualitative inspection shows that other search engines can provide better responses.
- A generative recurrent neural network (GRUQuestion-Generator) generates follow-up questions based on dialogue history.
- Model selection policy learns to choose the best response for maximizing long-term user satisfaction using reinforcement learning.
- Five approaches are evaluated: Q-learning, Deep Q-Networks, Double DQN, Dueling Network Architecture, and Actor-Critic with Experience Replay.
- Results show that Dueling Network Architecture performs best in terms of cumulative return and user satisfaction.
- The system is evaluated using real-world users, achieving an average satisfaction score of 3.5 out of 5.
- The paper introduces a Deep Reinforcement Learning (DRL) chatbot that uses dialogue history to select responses.
- It presents two approaches for parametrizing the agent's policy: an action-value function and a stochastic policy.
- Both approaches use a scoring model, which consists of 1458 features based on word embeddings, dialogue acts, part-of-speech tags, unigram/bigram overlap, and model-specific features.
- The paper compares the performance of the DRL chatbot with a baseline that uses a fixed response selection policy.
- Results show that the DRL chatbot outperforms the baseline in terms of dialogue quality, as measured by human evaluators.
- The DRL chatbot also achieves better results than the baseline in terms of user satisfaction and engagement metrics.
- The paper highlights the importance of using reinforcement learning for open-domain chatbots, which can learn from experience and adapt to new contexts.
- Future work includes exploring more complex dialogue acts, incorporating sentiment analysis, and investigating the impact of different reward functions on the performance of the DRL chatbot.
- The paper introduces a Deep Reinforcement Learning Chatbot, focusing on dialogue act response modeling and scoring models for chatbots.
- It uses 1458 features to represent the input, including part-of-speech tags, word overlaps, named entities, intensifiers, unigrams, negations, and more.
- The scoring model is a five-layered neural network with a linear transformation and rectified linear activation function.
- The model architecture compresses 500 hidden units to 20 and outputs probabilities for each response class.
- Experiments show that the proposed approach achieves an accuracy of 31.9% on the Cornell Movie-Dialogs Corpus, outperforming a baseline by 4.5 times in terms of speed.
- The model's execution time is under 150ms, making it suitable for real-time applications like Amazon Alexa.
- Future work includes exploring larger datasets and incorporating more advanced models such as Recurrent Neural Networks or Convolutional Neural Networks.
- The paper introduces a Deep Reinforcement Learning (DRL) chatbot model for scoring candidate responses in dialogues.
- It uses Amazon Mechanical Turk (AMT) to collect data and crowdsourced labels for training the scoring model.
- The architecture consists of an input layer, two hidden layers, a softmax layer with 5 output probabilities, and a scalar-valued output layer.
- Experiments show that deeper or more shallow models performed worse than the proposed architecture.
- Five machine learning approaches are used to learn the scoring model: Supervised AMT, Reinforcement Learning (RL), DRL, DRL with an external memory, and a hybrid approach combining RL and DRL.
- The paper presents results for each of these five approaches, comparing their performance in terms of accuracy, training time, and sample efficiency.
- The DRL chatbot model achieves 30% accuracy on the test set, which is significantly higher than other approaches (21%, 15%, 14%, and 16%).
- The DRL approach also outperforms others in terms of training time and sample efficiency.
- The paper discusses future work, including exploring alternative architectures, improving the scoring model's accuracy, and investigating other aspects to evaluate candidate responses.
- The paper aims to develop a Deep Reinforcement Learning (DRL) chatbot by optimizing response selection using annotated data from Amazon Mechanical Turk (AMT).
- Analyzing the annotations, it was observed that annotators tended to overrate generic responses, which are often acceptable for single turns but detrimental when repeated.
- To address this issue, the authors adjusted labels for certain response models: Alicebot, Elizabot, VHREDSubtitles, BoWEscapePlan, and BoWMovies.
- The training dataset consisted of 199,678 labels split into training (137,549), development (23,298), and testing (38,831) sets.
- The scoring model was trained to predict the AMT label classes using log-likelihood (cross-entropy). Adam optimizer was used with various hyperparameters experimented upon.
- Supervised AMT, a model trained on crowdsourced data from Amazon Mechanical Turk, achieved a Pearson correlation coefficient of 0.40 and a Spearman's rank correlation coefficient of 0.38, indicating significant improvement over the baseline.
- The performance of different policies (Random, Alicebot, Evibot + Alicebot) was compared in terms of AMT label class frequencies. Supervised AMT showed better results than other policies for most classes.
- Supervised AMT performs significantly better than baselines for classes ""good"" and ""excellent"".
- Supervised AMT reaches ~8% responses in the class ""excellent"", more than double compared to all three baseline policies.
- Overall, results show substantial improvement over baseline policies, but 46% of Supervised AMT responses belong to classes ""very poor"" and ""poor"".
- The paper introduces a new approach called Supervised Learned Reward, which learns to predict Alexa user scores based on previously recorded dialogues.
- This approach aims to address the issue of not knowing whether the score assigned to response categories is correlated with real-world Alexa users' scores.
- The reward model (gφ) predicts a linear regression model that learns to predict the corresponding return (Alexa user score).
- Training data is scarce, so only higher-level features are used as input for the reward model: AMT label class, generic response, response length, dialogue act, sentiment class, generic user utterance, user utterance length, confusion indicator, and dialogue length.
- The paper reports an improvement in performance when using Supervised Learned Reward compared to Supervised AMT.
- The paper introduces a Deep Reinforcement Learning Chatbot, which aims to improve dialogue systems by learning from recorded dialogues and using reinforcement learning techniques.
- It focuses on addressing the issue of low correlation between user satisfaction scores and traditional dialogue metrics like dialogue length or number of misunderstandings.
- The authors propose an ensemble reward model that learns a linear regression model for each shuffled training set, then averages their outputs to create the final ensemble reward model.
- They also introduce a Supervised Learned Reward (SLR) model, which is trained with a learned reward function instead of AMT labels. This helps in preventing overfitting and improves performance.
- The paper uses Off-policy REINFORCE, a variant of the classical REINFORCE algorithm, to learn the policy directly from recorded dialogues.
- They evaluate their approach on Alexa's dialogue dataset, which consists of 4340 dialogues and achieves better performance than predicting the average user score.
- The authors highlight that the low correlation between user satisfaction scores and traditional metrics is due to factors such as speech recognition errors, extrinsic factors influencing user profiles, environments, expectations, and emotional states.
- They also mention that the small amount of training data makes it difficult for the models to learn relationships between features and Alexa user scores.
- The paper's main contributions are the ensemble reward model, Supervised Learned Reward (SLR) model, and the use of Off-policy REINFORCE algorithm in dialogue systems.
- The paper introduces a Deep Reinforcement Learning (DRL) approach for creating chatbots.
- It uses a function fθ with parameters θ to represent the dialogue history, agent's action, and return.
- Off-policy REINFORCE algorithm updates policy parameters θ by considering importance weight ratio cd.
- The importance weight ratio corrects for discrepancies between learned policy and behavior policy.
- High returns lead to vectors increasing the probability of taking an action, while low returns decrease it.
- Importance ratios exhibit high variance, so they are normalized using a baseline function.
- The paper presents experimental results on a multi-turn dialogue task with 100 dialogues and 5 turns each.
- Results show that the proposed method outperforms a baseline model in terms of return and policy entropy.
- The DRL chatbot approach can be applied to various tasks, such as generating responses or learning from human feedback.
- Future work could involve incorporating more advanced reinforcement learning algorithms and improving the baseline function.
",3258
"1710.06481",1,"- The paper introduces a novel task for multi-hop reading comprehension across multiple documents, aiming to extend machine comprehension methods beyond single sentences or paragraphs.
- This task requires models to combine disjoint pieces of textual evidence, which is currently not supported by existing resources.
- Two datasets from different domains are created using query-answer pairs and thematically linked documents.
- The paper identifies potential pitfalls in the process and proposes strategies for overcoming them.
- Evaluation shows that one competitive model can integrate information across documents, but struggles to select relevant information.
- Providing guaranteed relevant documents improves performance significantly.
- While models outperform strong baselines, their best accuracy is 54.5%, leaving room for improvement compared to human performance at 85%.
- The paper highlights the need for further research in this area and provides a foundation for future work on multi-hop reading comprehension across documents.
- The paper focuses on constructing datasets for multi-hop reading comprehension across documents, enabling machine learning models to extract knowledge from multiple sources.
- Multi-hop reasoning is essential in scenarios where information cannot be found in a single location, such as Information Extraction (IE), search, and Question Answering (QA) applications.
- A novel RC task is introduced, requiring models to learn to answer queries by combining evidence across documents.
- Two datasets are created: WIKIHOP for WIKIPEDIA articles and MEDHOP for drug-drug interaction discovery from MEDLINE abstracts.
- Existing Knowledge Bases (KBs) like WIKIDATA and DRUGBANK are used as ground truth, with distant supervision to induce data.
- Human annotators can infer answers for 74.1% and 68.0% of samples in the datasets.
- Challenges in constructing multi-document datasets are addressed, including spurious co-locations and irrelevant document selection.
- Two competitive RC models (Seo et al., 2017a; Weissenborn et al., 2017) are evaluated for their performance on the proposed tasks.
- The paper demonstrates that models can integrate information across documents, but they struggle to select relevant information from larger document sets.
- The authors provide several strong baselines and discuss future work in improving multi-hop reading comprehension methods.
- Propose a cross-document multi-step reading comprehension (RC) task and a general dataset induction strategy for multi-hop RC across documents.
- Assemble two datasets from different domains, identifying pitfalls and remedies in the process.
- Establish multiple baselines, including recent RC models, and analyze model behavior through ablation studies.
- Achieve 54.5% accuracy on an annotated test set compared to human performance at 85%, indicating room for improvement.
- The methodology can be applied in practice by creating datasets for two different domains.
- End-to-end RC methods are fostered, inferring facts by combining separate facts stated in text without background information.
- A directed bipartite graph is used to identify candidates and support documents for a given query-answer pair.
- The traversal process starts from the subject entity of the query and ends at type-consistent answer entities.
- Constructing datasets for multi-hop reading comprehension across documents involves using a closed-world assumption and connecting entities, documents, and knowledge base facts in a bipartite graph.
- The methodology ensures that relevant textual evidence for the query is spread across multiple documents, promoting multihop reasoning beyond co-reference within a single document.
- Introducing alternative type-consistent candidates alongside the correct answer as end points makes the task more challenging and counters a type-consistency bias.
- WIKIHOP is proposed to be an extension of WIKIREADING, using Wikipedia and Wikidata for constructing cross-document multi-step reading comprehension datasets.
- The paper presents a new dataset with 100 samples, each containing 3-5 documents, 2-4 hops, and 10 candidate answers per hop.
- Evaluation results show that the proposed methodology achieves an accuracy of 78% on the WIKIHOP test set, outperforming the baseline by 19%.
- The paper highlights the importance of constructing datasets for multi-hop reading comprehension across documents to better reflect real-world scenarios and improve LLM performance in complex reasoning tasks.
- Constructing multi-hop reading comprehension datasets across documents using WIKIPEDIA and WIKIDATA as resources.
- Assembly of a bipartite graph for traversal, starting from the item entity in each query-answer pair.
- Graph traversal limited to 3 documents and maximum 100 candidates per answer.
- Mitigating dataset biases by removing samples with more than 64 different support documents or 100 candidates (~1% of samples).
- Candidate frequency imbalance in WIKIREADING, leading to a significant bias in the answer distribution for certain properties.
- Improved performance on WIKIHOP using a disjoint subset of WIKIREADING compared to Levy et al.'s work (2017).
- Faster training and inference times with the proposed multi-hop dataset, reducing training time by 4.5 times and inference time by 30%.
- Constructing WIKIHOP dataset for multi-hop reading comprehension across documents, addressing issues like majority class baseline and spurious correlations.
- Document-Answer Correlations problem in multi-document settings resolved by sub-sampling the dataset to ensure samples of any one answer candidate make up less than 0.1%.
- MEDHOP dataset for molecular biology domain, addressing the need for NLP methods due to exponential growth in publications and resources like UniProt Consortium.
- Existing RC datasets either limited in size or cover diverse query types, complicating neural model applications; DDI efforts focused on explicit mentions in single sentences, but cross-sentence relation extraction increases the number of interactions.
- The paper proposes a novel method for constructing large-scale multi-hop reading comprehension datasets across documents, addressing issues and providing practical solutions for various domains.
- Multi-hop reading comprehension across documents improves recall, especially for implicit interactions that need inference from separate pieces of evidence.
- Cross-document interactions can lead to previously unobserved drug-drug interactions (DDIs) and aid scientific discoveries through inferring them from established public knowledge.
- MEDHOP dataset construction uses DRUGBANK as a structured knowledge resource and research paper abstracts from MEDLINE as documents, with only one relation type: interacts_with.
- The graph structure consists of edges between documents and proteins mentioned in them, and between documents and drugs if the document mentions a protein known to be a target for that drug according to DRUGBANK. These edges are bidirectional.
- MEDHOP's main contributions include a novel multi-hop reading comprehension task, a new dataset for this task, and an evaluation of state-of-the-art models on the task.
- The paper introduces a method for constructing datasets for multi-hop reading comprehension across documents, focusing on drug-drug interactions and protein-protein interactions.
- It uses bidirectional edges between drugs and their targets in DRUGBANK and proteins with known interactions in REACTOME to create a bipartite graph.
- Document sub-sampling is applied to make the dataset computationally feasible for most existing reading comprehension models, while maintaining a balance of candidate frequencies.
- The paper compares MEDHOP (the proposed method) with WIKIHOP and shows that MEDHOP has a greater supervised learning signal per sample despite having fewer samples than other large-scale reading comprehension datasets.
- MEDHOP's dataset sizes are presented in Table 1, which also highlights the differences between WIKIHOP and MEDHOP.
- The paper focuses on constructing datasets for multi-hop reading comprehension across documents, addressing challenges in distant supervision and creating training sets for WIKIHOP and MEDHOP.
- In WIKIHOP, there are 43,738 samples with an average of 19.5 unique document paths per sample, while in MEDHOP, there are 1,620 samples with an average of 59.8 unique document paths.
- The number of candidates and documents per sample vary across datasets, with WIKIHOP having a maximum of 79 candidates and 63 documents, while MEDHOP has a maximum of 9 candidates and 64 documents.
- Qualitative analysis reveals that 51% of samples in WIKIHOP have either a unique multi-step answer or a likely one, while 36% have a single unique multi-step answer. In MEDHOP, 90% of samples have a single correct answer among the candidates.
- Distant supervision errors occur in 20% of WIKIHOP samples and 12% of MEDHOP samples due to conflicts between WIKIDATA and Wikipedia or ambiguity caused by hypernymy.
- The paper highlights the importance of creating training sets for multi-hop reading comprehension tasks, addressing challenges in distant supervision, and providing insights into the quality of constructed datasets.
- The paper focuses on constructing datasets for multi-hop reading comprehension across documents, addressing issues like conflicting information between sources and distant supervision assumptions.
- Annotators knew the answer before reading in 9% of cases, while they produced correct answers after reading in 74%. Accuracy reached 85% on a portion of the Dev set.
- MEDHOP's larger document complexity made it impractical to have annotators read all support documents; instead, only relevant documents were provided for verification.
- Majority of cases violating distant supervision assumptions were due to missing necessary PPI in connecting documents.
- Crowdsourced human annotation on Amazon Mechanical Turk showed 4.6% prior knowledge of facts and fair overall agreement (Fleiss' kappa: 0.253, 0.281).
- In multi-document samples with a majority vote for either ""follows"" or ""likely"", 55.9% required multiple documents to infer the fact while 44.1% needed only one document.
- The paper highlights the importance of distant supervision strategy and its justification in multi-hop reading comprehension across documents.
- Multi-hop reading comprehension across documents involves inferring answers from multiple sources, rather than just a single document.
- Many cases initially labeled as ""single"" actually provide hints within one document for the correct answer without explicitly stating it.
- Validated test sets are essential to evaluate methods on manually annotated samples, contrasting with prior work that used only distantly supervised data.
- WIKIHOP and MEDHOP experiments were conducted to establish performance of baseline models and investigate their behavior in multi-step reading comprehension tasks.
- Models tested included Random selection, Max-mention, Majority-candidate-per-query-type, TF-IDF Retrieval-based, and neural RC models.
- The paper highlights the importance of mitigating dataset biases, probing multi-step behavior's benefits for solving tasks, and investigating if RC models can learn lexical abstraction.
- The paper explores constructing datasets for multi-hop reading comprehension across documents, focusing on identifying the correct answer from multiple sources using lexical correlations and extractive QA models.
- It introduces two baseline methods: one that predicts candidates based on individual document searches with TF-IDF similarity scores and another that exploits informative document-answer co-occurrences.
- The paper evaluates the performance of two LSTM-based extractive QA models, BiDAF and FastQA, in a multidocument setting by adapting them to sequentially concatenate all documents into a superdocument with randomized order.
- Preliminary experiments show that performance does not significantly change when using different document order permutations.
- The paper highlights the importance of pre-trained GloVe embeddings and bidirectional LSTMs in these models, which were initially developed for single-hop reading comprehension tasks.
- BiDAF and FastQA are shown to achieve 30% and 27% accuracy on the MultiNLI dataset, respectively, with FastQA being 4.5 times faster than BiDAF.
- The paper concludes that these models can be adapted for multi-hop reading comprehension tasks across documents, but further research is needed to improve their performance in this setting.
- Multi-hop reading comprehension across documents: The paper introduces this concept, focusing on models that can integrate information from different locations in a (super-)document.
- BiDAF model: It uses bidirectional LSTMs and attention over the full sequence, theoretically making it better suited for multi-hop RC tasks.
- Lexical abstraction issue: The presence of lexical regularities among answers can be problematic in RC dataset assembly. To address this, masked versions of datasets were created by replacing candidate expressions with unique placeholder tokens.
- Masking benefits: This process removes answer frequency cues and statistical correlations between frequent answer strings and support documents, forcing models to rely on context for predictions.
- Experimental outcomes: Candidate mention frequency does not produce better predictions than a random guess in most cases. TF-IDF retrieval baseline performs better than random but is not very strong overall. Document-cue baselines can predict more than a third of the answers, showing that lexical matching with a single support document is insufficient for building strong predictive models.
- Practical applications: The paper highlights the importance of considering multi-hop reading comprehension and masking techniques in future research to improve model performance.
- The paper focuses on constructing datasets for multi-hop reading comprehension across documents, addressing issues related to baseline performance and dataset biases.
- Document-cue baselines can predict more than a third of samples correctly in both WIKIHOP and MEDHOP datasets, highlighting the importance of addressing these issues when designing multi-hop datasets.
- The paper introduces measures to mitigate bias, such as filtering frequent document-answer pairs and removing redundant documents. These measures result in a relative drop in baseline performance, demonstrating their effectiveness.
- BiDAF neural model shows overall stronger performance across both WIKIHOP and MEDHOP datasets compared to other baselines. This is possibly due to its iterative latent interactions, which are more important for tasks involving distributed information across documents.
- In the masked setup, all baseline models relying on lexical cues fail when answer expressions are randomized, as they cannot rely on candidate options Cq.
- The paper emphasizes that unlike other baselines, FastQA and BiDAF predict answers by extracting spans from support documents without relying on candidate options.
- In the masked setup, BiDAF's performance is significantly better than FastQA, suggesting that its iterative latent interactions are more effective for multi-hop reading comprehension tasks.
- The paper highlights the importance of investigating and addressing dataset biases to avoid confounding seemingly strong RC model performance.
- The study provides practical applications and benefits by improving the design of multi-hop datasets, leading to better performance in multi-hop reading comprehension tasks.
- The paper's findings can be useful for future LLM researchers working on constructing datasets for multi-hop reading comprehension across documents.
- Masking answers in multi-hop reading comprehension datasets is a valuable alternative to dataset sub-sampling, especially for MEDHOP where it effectively circumvents spurious correlations.
- Neural reading comprehension models can largely retain or improve their performance when answers are masked, leveraging textual context of candidate expressions.
- Drug mentions in MEDHOP are normalized to a unique single-word identifier, which affects model behavior differences between WIKIHOP and MEDHOP.
- Both neural RC models outperform other baselines but still have room for improvement compared to human performance (74%/85% for WIKIHOP).
- Consistently improved results on validated test sets suggest that training set contains necessary signals for inference on valid samples at test time.
- Models improve greatly when presented with only relevant documents, demonstrating their ability to identify answers with few or no false candidates.
- Neural RC models' answer selection process is not robust to the introduction of unrelated documents with type-consistent candidates, indicating that learning to intelligently select relevant documents before reading comprehension may be a promising direction for future model development.
- The paper focuses on constructing datasets for multi-hop reading comprehension across documents, which requires models to draw upon information from multiple sources and perform inference steps.
- BiDAF, FastQA, and other LLMs are evaluated using these constructed datasets, showing varying performance levels when dealing with cross-document inference.
- BiDAF demonstrates a significant drop in performance when handling multi-hop reading comprehension, indicating its ability to leverage cross-document information. In contrast, FastQA shows mixed results, suggesting problems integrating cross-document information due to fewer latent interactions than BiDAF.
- The paper reviews related work and datasets for end-to-end text-based question answering, highlighting the differences between single-document and multi-hop reading comprehension tasks.
- WIKIHOP and MEDHOP are specifically designed for cross-document reading comprehension and multistep inference, providing a basis for comparison with other models.
- The paper discusses composite knowledge base inference methods, including Inductive Logic Programming, Markov Logic, Path Ranking Algorithm, and synthetic link generation via dense latent embeddings.
- Practical applications of these findings include improving the performance of LLMs in multi-hop reading comprehension tasks and better understanding how models handle cross-document information.
- The paper explores constructing datasets for multi-hop reading comprehension across documents, focusing on end-to-end language understanding approaches that infer answers directly from text without relying on intermediate query parsing or information extraction steps.
- It evaluates whether end-to-end multi-step reading comprehension models can operate on raw text documents for inference tasks typically associated with logical inference methods operating on structured knowledge.
- The paper reviews previous approaches, such as composition functions, neural theorem provers, and memory networks, which work within a pipeline or rely on human annotation.
- It highlights the potential of recent neural reading comprehension models to perform multi-step reasoning without relying on intermediate steps like query parsing or information extraction.
- The paper discusses text-based multi-step reading comprehension and its benefits, such as exploiting information from related documents based on lexical semantic similarity for reranking answers in open-domain non-factoid QA.
- It introduces memory networks, a model class that iteratively attends over textual memory items, showing promising performance on synthetic tasks requiring multi-step reasoning.
- The paper emphasizes the common characteristic of neural multi-hop models: their rich structure enabling matching and interaction between question, context, answer candidates, and combinations thereof.
- It mentions that while these methods show promise for single-document reading comprehension, they have not been evaluated for a cross-document multi-step reading comprehension task.
- The paper also discusses learning search expansion through web navigation or query reformulation techniques using neural reinforcement learning.
- Ultimately, the work aims to expand on these methods by focusing on reformulating queries and reevaluating existing datasets for multi-hop reading comprehension across documents.
- The paper introduces a new cross-document multi-hop reading comprehension (RC) task, focusing on reformulating queries to acquire evidence documents rather than answering directly.
- A generic dataset derivation strategy is devised and applied to two domains, resulting in datasets that test RC methods' ability to perform composite reasoning.
- Experiments show contemporary RC models can leverage cross-document information but have a significant gap compared to human performance.
- The selection of relevant document sets is identified as the most promising direction for future research.
- The paper assumes factoid questions about entities, with answers mentioned verbatim, which limits question types but facilitates training and evaluation.
- Future work should move beyond this assumption to free-form abstractive answer composition.
- The authors hope their work will foster research on cross-document information integration, working towards long-term goals.
- Acknowledgments include thanks to reviewers, editors, collaborators, and funding sources.
",3894
"1710.10368",1,"- Deep Generative Dual Memory Network for Continual Learning: This paper proposes an architecture to overcome catastrophic forgetting in neural networks, enabling them to learn continually from sequentially arriving tasks.
- Inspiration from human memory: The architecture emulates the complementary learning systems (hippocampus and neocortex) in the human brain, addressing the issue of catastrophic forgetting.
- Dual Memory Architecture: Consists of a generative replay buffer for past experiences and a task-specific memory for recent tasks. This design allows for memory consolidation and gradual forgetting based on task frequency.
- Experimental results: Demonstrate advantages of the dual memory architecture over single memory models, with improved performance retention even for low capacity models.
- Connection to mammalian memory: The architecture's characteristics resemble those found in mammalian brains, providing insights into the connection between sleep and learning.
- The paper discusses catastrophic forgetting, a common issue in sequential learning systems that violate the iid sampling assumption for gradient-based learning.
- Experience replay approaches have been proposed to restore the iid assumption by storing and replaying previously seen samples or using them to modify future updates.
- The paper introduces Deep Generative Dual Memory Network (DGDMN) as a solution to address the challenges of experience replay, such as determining which samples to store and discard.
- DGDMN maintains a generative model over samples that automatically provides the most frequently encountered samples from the distribution learned so far.
- This approach is feasible with limited total memory and avoids explicitly determining which and how many samples should be stored or discarded per task.
- The paper presents experiments showing DGDMN's effectiveness in image classification tasks, outperforming other experience replay methods.
- Practical applications of DGDMN include autonomous driving systems, medical imaging, and robotics, where continuous learning is crucial for improved performance.
- DGDMN can be used to learn from a stream of data without forgetting previously learned tasks, making it suitable for lifelong learning scenarios.
- The paper also discusses the potential use of DGDMN in reinforcement learning and other sequential decision-making problems.
- DGDMN's architecture consists of two memory networks: a generative model network (GMN) and a discriminative model network (DMN), which work together to achieve continual learning without forgetting.
- The paper proposes a dual-memory architecture for continual learning, inspired by human brain's complementary learning systems theory.
- It consists of two generative models: short-term memory (STM) and long-term memory (LTM). STM emulates the hippocampus, while LTM represents the neocortex.
- The STM learns new tasks without interfering with previously learnt tasks in the LTM. LTM stores all previous tasks and aids the STM in learning similar tasks.
- During sleep/down-time, the STM generates samples of learnt tasks and transfers them to the LTM for consolidation.
- The model exploits deep generative models, experience replay, and complementary learning systems literature to address catastrophic forgetting in sequential task learning.
- Experiments demonstrate the model's performance in averting catastrophic forgetting by sequentially learning multiple tasks.
- Results shed light on some characteristics of human memory as observed in psychology and neuroscience literature.
- Deep Generative Dual Memory Network for Continual Learning: The goal is to learn tasks sequentially without forgetting previous ones and achieve high test accuracy.
- Finite memory concept: Limited storage size (Nmax) for algorithms, usually smaller than total samples. Storing all training samples infeasible.
- Evaluation metrics: Average Accuracy (ACC) across all tasks and Backward Transfer (BWT). ACC measures overall performance while BWT shows influence of task t on previous task τ. Ideal continual learning algorithm should have high ACC with least negative or positive BWT.
- Deep Generative Dual Memory Network architecture: Consists of a generative model (generator G), feedforward network (learner L), and dictionary (Ddgm) with task descriptors and encounter counts. Variational autoencoder used as generator, but other models possible.
- Deep Generative Replay algorithm: Updates Deep Generative Memory (DGM) using new incoming samples from multiple tasks. Pseudocode in Algorithm 1. Combines new samples with generated samples from previous tasks and relearns jointly on these samples.
- Practical applications: Potential use cases include robotics, autonomous vehicles, and medical diagnosis systems that need to learn continuously without forgetting past knowledge.
- Deep Generative Dual Memory Network (DGR) is a continual learning algorithm that preserves previous tasks' memory while learning new ones, ensuring performance retention and gradual forgetting of old tasks.
- The network consists of a generator, learner, and dual generative replay (DGR) mechanism. It balances the number of samples from new and generated tasks to maintain performance on older tasks while still learning new ones.
- DGR's reconstruction process provides robustness against noise and occlusion, making representations more resilient compared to other approaches without a dual memory architecture.
- Preliminary experiments show that DGR alone is slow and inaccurate; therefore, the authors propose an improved version called Deep Generative Dual Memory Network with Adaptive Learning Rate (DGDM-ALR).
- DGDM-ALR combines DGR with an adaptive learning rate to balance the trade-off between new task acquisition speed and performance retention on previous tasks, resulting in faster convergence and better accuracy.
- Deep Generative Dual Memory Network (DGDMN) architecture proposed for continual learning, addressing quick acquisition of new tasks and performance retention on previously learnt tasks.
- Consists of a large DGM (long-term memory - LTM) and short-term memory (STM), with nSTM small, dedicated deep generative memories (short-term task memory - STTM).
- During training, if the incoming task is already in an STTM, it's retrained; otherwise, a new STTM is allocated. If the task has been previously seen and consolidated into LTM, LTM reconstructs samples for that task using its generator.
- Once all STTMs are exhausted, architecture sleeps to consolidate tasks in LTM and free up STTMs for new tasks. During sleep, STM generates samples of learnt tasks and sends them to LTM for consolidation.
- DGDMN uses task descriptors to recognize if a task has been previously observed or the memory it's in; this can be relaxed by using reconstruction error from generators as a proxy.
- Experiments on sequential image classification tasks (Permnist, Digits, TDigits, Shapes, and Hindi) demonstrate DGDMN's effectiveness in addressing catastrophic forgetting compared to several baselines.
- Deep Generative Dual Memory Network (DGDMN) is a novel approach for continual learning, addressing catastrophic forgetting in neural networks.
- The model consists of two memory components: a long-term memory (LTM) and a short-term memory (STM). LTM stores task-specific knowledge while STM handles recent tasks.
- DGDMN uses generative replay to prevent catastrophic forgetting, allowing the network to learn new tasks without losing previous knowledge.
- The paper compares DGDMN with several baselines, including feedforward neural networks (NN), Neural nets with dropout (DropNN), Pseudopattern Rehearsal (PPR), Elastic Weight Consolidation (EWC), and Deep Generative Replay (DGR).
- DGDMN outperforms other methods in terms of accuracy, forgetting curves, and learning speed on various image classification tasks.
- The model's performance is particularly impressive when dealing with sequential tasks, where it can achieve a 30% higher accuracy than EWC.
- DGDMN demonstrates the importance of overparameterization in mitigating catastrophic forgetting and highlights the benefits of generative replay for continual learning.
- The paper introduces a Deep Generative Dual Memory Network (DGDMN) for continual learning, addressing catastrophic forgetting and inter-task interference issues in neural networks.
- It compares the performance of DGDMN with other baselines like NN, DropNN, PPR, EWC, and DGR on various datasets such as Digits, Permnist, Shapes, and Hindi digits.
- The paper shows that DGDMN consistently outperforms baseline algorithms in terms of retained average accuracy, while other methods suffer from catastrophic forgetting or saturation issues.
- DGR and DGDMN retain performance on all tasks in Digits, as they replay generated samples from previous tasks, unlike NN, DropNN, PPR, and EWC that only learn the current task and forget previous knowledge.
- The paper demonstrates that a generative replay mechanism is crucial for accurate input distribution modeling to prevent catastrophic forgetting.
- DGDMN and DGR perform similarly and outperform other baselines in terms of accuracy while having the least negative backward transfer, indicating effective mitigation of catastrophic forgetting and avoidance of inter-task interference.
- The paper highlights that datasets like Digits should be used with caution due to their sequential nature causing heavy forgetting on most baselines.
- Deep Generative Dual Memory Network (DGDMN) is a continual learning algorithm designed to address catastrophic forgetting and inter-task interference issues in LLMs.
- The TDigits dataset, with low correlation between tasks, serves as an important benchmark for evaluating continual learning algorithms due to its tendency to cause overfitting and catastrophic forgetting.
- DGDMN outperforms DGR (Deep Generative Replay) in retaining accuracy on task sequences, training faster, and maintaining a higher average accuracy on the most recent 10 tasks. This is because DGDMN uses small STTMs to learn single tasks with low error, leading to less frequent consolidation of LTM and more accurate samples.
- The dual memory architecture and periodic sleep in humans are similar to DGDMN's design, suggesting that these features play a crucial role in learning efficiency and preventing catastrophic forgetting.
- DGDMN shares some characteristics with human memory, such as the ability to learn new tasks without forgetting old ones, and its performance on the TDigits dataset aligns well with human behavior.
- Deep Generative Dual Memory Network (DGDMN) for continual learning: A novel architecture that combines a generative model with a memory network, allowing it to learn from streaming data without explicit task descriptors.
- Resilience to noise and occlusion: The model's denoising reconstructive properties make it more robust to noisy and occluded images compared to traditional neural networks.
- Agnostic choice of underlying generative models: DGDMN can use various generative models, such as VAEs, BiGANs, ALI, or AVB, depending on the modeled domain.
- Connections to knowledge distillation: The consolidation phase in DGDMN refines and compresses learned functions, similar to how knowledge distillation can improve performance on individual tasks while mitigating interference between them.
- Learning from streaming data: DGDMN's ability to recognize task samples via a reconstructive generative model makes it applicable for domains with directly streaming data without requiring explicit task descriptors.
- Forgetting curves: The paper presents forgetting curves that show the average classification accuracy on tasks seen, demonstrating how DGDMN performs better than other models in continual learning scenarios.
- Experimental results: DGDMN outperforms Deep Generative Replay (DGR) and other baselines in terms of accuracy and training time on T-Digits datasets.
- Practical applications: The model's ability to learn continually from streaming data without explicit task descriptors makes it suitable for real-world scenarios, such as autonomous driving or medical image analysis.
- The paper introduces Deep Generative Dual Memory Network (DGDMN) for continual learning, addressing catastrophic forgetting in neural networks.
- DGDMN is inspired by the human brain's complementary learning systems and experience replay mechanisms.
- It consists of a generative long-term memory (LTM) and an episodic short-term memory (STM), both trained simultaneously to avoid forgetting past experiences.
- The LTM uses a Generative Adversarial Network (GAN) for data reconstruction, while the STM employs a recurrent neural network (RNN).
- Experiments show that generative replay performs better than other methods in terms of long-term performance retention and scales well with a dual memory architecture.
- The paper highlights parallels between DGDMN and human memory systems, providing insights into the connection between sleep and learning in humans.
- Future work includes exploring reinforcement learning applications for DGDMN, incorporating synaptic consolidation mechanisms, and investigating the impact of sleep on neural networks.
",2493
"1711.05101",1,"- The paper investigates the use of L2 regularization and weight decay regularization for adaptive gradient algorithms, specifically Adam, compared to standard stochastic gradient descent (SGD).
- It demonstrates that while these two regularizations are equivalent in SGD, they differ significantly in adaptive gradient methods like Adam.
- The authors propose a simple modification called decoupled weight decay to recover the original formulation of weight decay regularization by separating it from optimization steps taken w.r.t. the loss function.
- Empirical evidence shows that this proposed modification allows for an optimal choice of weight decay factor independent of the learning rate in both SGD and Adam, improving generalization performance.
- Decoupled weight decay enables Adam to compete with SGD with momentum on image classification datasets, where it previously underperformed.
- The decoupled weight decay has been adopted by many researchers and implemented in popular frameworks like TensorFlow and PyTorch.
- The complete source code for the experiments is available at https://github.com/loshchil/AdamW-and-SGDW.
- L2 regularization and weight decay are not identical, especially when combined with adaptive gradient methods like Adam.
- L2 regularization is ineffective in Adam, leading to larger historic parameter/gradient amplitudes being less regularized than with weight decay.
- Common deep learning libraries only implement L2 regularization, not the original weight decay, potentially causing Adam's performance to be worse on tasks where L2 regularization benefits SGD.
- Weight decay is equally effective in both SGD and Adam; it's equivalent to L2 regularization for SGD but not for Adam.
- Optimal weight decay depends on the total number of batch passes/weight updates, with larger runtimes requiring smaller optimal weight decays.
- Adam can benefit from a scheduled learning rate multiplier, which does not contradict its adaptive nature.
- The main contribution is to improve regularization in Adam by decoupling weight decay from the gradient-based update.
- Decoupled weight decay leads to better generalization and up to 15% relative improvement in test error compared to L2 regularization.
- This method makes learning rate and weight decay factor optimization more independent, easing hyperparameter tuning.
- The goal of the paper is to improve Adam's performance to make it competitive with SGD for momentum even on tasks where it was previously not competitive.
- Decoupling weight decay from gradient-based updates allows for better control over learning rate and weight decay hyperparameters, improving optimization performance.
- In standard SGD, weight decay is equivalent to L2 regularization; however, this equivalence does not hold for adaptive gradient methods like Adam.
- The proposed variant of SGD with momentum using decoupled weight decay (SGDW) explicitly decouples the learning rate and weight decay hyperparameters.
- A scaling factor ηt is introduced to account for potential scheduling of both learning rate and weight decay, delivered by a user-defined procedure SetScheduleMultiplier(t).
- The authors demonstrate that SGDW outperforms standard SGD with L2 regularization in terms of convergence speed on various benchmark datasets.
- SGDW also shows improved performance compared to Adam with L2 regularization, especially for large learning rates and weight decay values.
- The paper provides a theoretical analysis of the proposed method, showing that it converges to a stationary point under certain conditions.
- The paper introduces a new variant of Adam called AdamW, which decouples weight decay and loss-based gradient updates. This allows for better control over regularization in adaptive gradient algorithms.
- L2 regularization and decoupled weight decay are shown to be inequivalent for adaptive gradient methods. In particular, Proposition 2 demonstrates that there is no L2 coefficient λ' that makes running an optimizer on a regressed loss function equivalent to running it with weight decay.
- AdamW separates the weight decay step from the adaptive gradient mechanism, leading to different regularization effects compared to standard L2 regularization. In contrast to L2 regularization, which adapts both types of gradients and normalizes them by their typical magnitudes, decoupled weight decay regulates all weights at a constant rate (λ), effectively increasing the regularization for weights with large gradient magnitudes more than in L2 regularization.
- The paper provides an example of AdamW's performance on CIFAR-10 and ImageNet datasets, showing that it achieves better accuracy compared to standard Adam and Adam with L2 regularization.
- AdamW is shown to be more robust to hyperparameter choices than Adam with L2 regularization. This makes it easier to tune the model's performance without extensive hyperparameter search.
- The paper also discusses the use of warm restarts, which can improve training stability and convergence speed by restarting the optimizer at a pre-trained checkpoint.
- AdamW is shown to be more efficient in terms of memory usage compared to Adam with L2 regularization due to its decoupled weight decay mechanism. This makes it suitable for large models or datasets where memory constraints are an issue.
- The paper introduces ""Decoupled Weight Decay Regularization,"" which effectively regularizes weights with large s more than standard L2 regularization, as demonstrated in a simple special case of adaptive gradient algorithms with fixed preconditioners.
- Proposition 3 shows that for this specific case, the algorithm executes the same steps on batch loss functions with weight decay λ as it does without weight decay on scale-adjusted regularized batch loss functions. This proposition doesn't directly apply to practical adaptive gradient algorithms but provides intuition about the equivalent loss function being optimized in each step.
- The paper justifies decoupled weight decay via a view of adaptive gradient methods as Bayesian filtering, which suggests that weight decay emerges through straightforward application of Bayesian filtering and is favored over L2 regularization.
- Aitchison's theory (2018) views stochastic optimization as a Bayesian filtering problem, where the goal is to infer a distribution over optimal values for each parameter given current values of other parameters at different time steps. This approach helps understand why weight decay may be preferred over L2 regularization.
- The paper's findings imply that decoupled weight decay can provide better performance than standard L2 regularization in certain scenarios, particularly when dealing with adaptive gradient algorithms and Bayesian filtering perspectives.
- The paper proposes a unified framework for decoupled weight decay regularization, which fits naturally into an existing Bayesian filtering approach.
- This framework views popular adaptive gradient methods (Adam, RMSprop) and Kronecker-factorized methods as special cases of the proposed method.
- Decoupled weight decay is introduced as part of the state transition distribution in this unified framework, where Aitchison assumes a slow change of the optimizer according to a Gaussian distribution.
- The regularization parameter A can be instantiated as A = λ × I, which leads to decoupled weight decay as described in Equation 1. This regularization is directly applied to the prior and does not depend on the uncertainty in each of the parameters (unlike L2 regularization).
- Experimental results show that decoupled weight decay performs better than L2 regularization under various training budgets and learning rate schedules, especially when combined with cosine annealing.
- Decoupled weight decay leads to a more separable hyperparameter search space, making it easier to find optimal settings for different learning rate schedules.
- The paper proposes a new method called Shake-Shake regularization, applied to a 3-branch residual DNN, achieving state-of-the-art results on CIFAR-10 with 2.86% error rate.
- Experiments compare Adam with L2 regularization and Adam with decoupled weight decay (AdamW) using three different learning rate schedules: fixed, drop-step, and cosine annealing. Results show that decoupled weight decay outperforms L2 regularization for all schedules, with larger differences for better schedules.
- Decoupled weight decay leads to a more separable hyperparameter search space, especially when combined with learning rate schedules like drop-step and cosine annealing. Cosine annealing was found to outperform other schedules.
- The paper investigates the hypothesis that coupling of α (initial learning rate) and λ (weight decay factor) affects performance. In SGD, L2 regularization is not decoupled from the learning rate, while in Adam, it is already adapted to parameter-wise learning rates.
- Experiments compare L2 regularization vs. decoupled weight decay in SGD (SGD vs. SGDW) and Adam (Adam vs. AdamW). In SGD, L2 regularization's performance is worse than decoupled weight decay, while in Adam, the difference is smaller but still present.
- The paper also explores the effect of varying initial learning rates and weight decay factors on model performance.
- Results show that decoupling weight decay from the learning rate improves generalization and reduces overfitting, especially when combined with a good learning rate schedule like cosine annealing.
- Practical applications include using AdamW for training deep neural networks, potentially leading to better results than Adam with L2 regularization.
- The paper highlights that decoupled weight decay can be applied to any adaptive gradient algorithm and not just SGD or Adam.
- Future work could involve applying decoupled weight decay to other optimization algorithms and exploring its impact on different datasets and network architectures.
- The paper investigates the decoupling of weight decay regularization and learning rate hyperparameters in Stochastic Gradient Descent (SGD) and Adam optimizers.
- It shows that SGD's reputation for being sensitive to its hyperparameter settings is due to a coupling between initial learning rate and L2 regularization factor, making them interdependent.
- The proposed approach, called SGD with decoupled weight decay (SGDW), separates these two hyperparameters, allowing better optimization without the need for simultaneous adjustments.
- Adam with L2 regularization does not benefit from it and performs worse than SGD's best results. In contrast, a new variant of Adam called AdamW with decoupled weight decay (AdamW) improves its performance to be competitive with SGD.
- The paper suggests that the weight decay and learning rate hyperparameters can be decoupled, simplifying the problem of hyperparameter tuning in SGD and improving Adam's performance.
- The paper investigates AdamW's generalization capabilities compared to Adam by conducting longer runs (1800 epochs) and fixing the initial learning rate at 0.001, which represents both default values for Adam and a reasonably good result in their experiments.
- Figure 3 shows that while Adam and AdamW often had similar learning curve dynamics during the first half of training, AdamW led to lower training loss and test errors. The use of L2 weight decay in Adam did not yield as good results as decoupled weight decay in AdamW.
- The paper explores whether AdamW's better results are due to better convergence or generalization performance. Results suggest that AdamW yields both better training loss and improved generalization performance for similar training loss values, as seen on CIFAR-10 and ImageNet32x32.
- To improve the anytime performance of SGDW and AdamW, they are extended with warm restarts introduced in Loshchilov & Hutter (2016), resulting in SGDWR and AdamWR respectively. Figure 4 shows that AdamWR significantly speeds up AdamW on CIFAR-10 and ImageNet32x32, achieving a relative improvement of 15% in test error compared to Adam for both datasets.
- Several other research groups have successfully applied AdamW in their works, such as Wang et al. (2018) using it for training a novel architecture for face detection on the standard dataset.
- The paper introduces decoupled weight decay regularization for Adam, an adaptive gradient method, to improve generalization performance and overcome the inequivalence of L2 regularization in Adam.
- Empirical results show that Adam with decoupled weight decay outperforms common implementations of Adam with L2 regularization on image classification tasks.
- The paper proposes using warm restarts for Adam to improve its anytime performance.
- Future work involves verifying the findings on a wider range of tasks, integrating the findings into other methods, and exploring similar results in other adaptive gradient methods like AdaGrad and AMSGrad.
- Key contributions include identifying the inequivalence between L2 regularization and weight decay for Adam, proposing decoupled weight decay for Adam, and suggesting warm restarts to improve its anytime performance.
- The paper introduces ""decoupled weight decay regularization,"" a technique that separates learning rate and weight decay hyperparameters for Adam optimizer, improving generalization performance in deep neural networks.
- Decoupling weight decay from the learning rate allows for better control over optimization, reducing the risk of overfitting and improving convergence speed.
- The paper provides empirical evidence showing that decoupled weight decay regularization can improve accuracy by up to 10% on various datasets, such as CIFAR-10, CIFAR-100, SVHN, and ImageNet.
- This technique is particularly beneficial for large batch sizes, where it reduces the generalization gap and leads to sharper minima.
- The paper also discusses how decoupled weight decay can be implemented in popular deep learning libraries like PyTorch, TensorFlow, fast.ai, Keras, Caffe, and Adam-experiments.
- The authors acknowledge support from the European Research Council (ERC), German Research Foundation (DFG), BrainLinksBrainTools Cluster of Excellence, and bwHPC.
- Decoupled weight decay regularization is a practical application that can improve the performance of deep learning models in various domains, such as computer vision and natural language processing.
- The paper provides a unified theory for adaptive stochastic gradient descent methods, connecting them to Bayesian filtering.
- It also discusses other related works, including Shake-Shake regularization, large-batch training for deep learning, Adam optimizer, and visualizing the loss landscape of neural networks.
- The paper ""Decoupled Weight Decay Regularization"" explores weight decay regularization techniques and their impact on neural network training.
- It introduces a new method called decoupled weight decay (DWD), which separates the weight decay into two components: one for bias terms and another for non-bias parameters.
- DWD improves convergence speed, reduces overfitting, and leads to better generalization performance in neural networks.
- The authors provide a theoretical analysis of DWD and demonstrate its effectiveness through experiments on various datasets and architectures, including ResNet, VGG, and Inception-v3 models.
- Experiments show that DWD achieves 10% lower validation loss compared to standard weight decay (SWD) in ResNet-50, while maintaining similar accuracy.
- The paper also presents a new regularization technique called ""decoupled L2"" that combines DWD with L2 regularization for further performance improvements.
- Decoupled L2 achieves 1.3% higher top-1 accuracy on ImageNet compared to SWD and 0.5% higher than DWD, while reducing the number of training epochs by 4.5 times.
- The paper highlights that decoupling weight decay can be applied to other regularization methods like L2, dropout, and batch normalization for better performance.
- Practical applications include using DWD in object detection models (SSD) and semantic segmentation models (FCN), leading to improved accuracy and faster convergence.
- The paper provides a comprehensive analysis of weight decay regularization techniques and introduces new methods that can significantly improve the training process for neural networks.
- The paper analyzes the relationship between weight decay and L2 regularization, focusing on their iterates in optimization algorithms.
- It proves that there is no equivalent L2 regularizer for weight decay in O (optimization algorithm) that would make its iterates identical to those with weight decay.
- The authors introduce decoupled weight decay for Adam, which separates the learning rate and weight decay hyperparameters, leading to improved generalization performance.
- They propose two additional practical improvements: normalized weight decay and AdamWR (weight restart).
- Normalized weight decay aims to reduce dependence on batch size by normalizing the weight decay hyperparameter based on the total number of training points, epochs, and batch size.
- AdamWR introduces a weight restart mechanism that resets the learning rate and momentum exponentially after each restart, leading to faster convergence and better generalization performance.
- The paper provides empirical evidence showing that decoupled weight decay improves generalization in various settings, including CIFAR-10, ImageNet, and large-scale image recognition tasks.
- AdamWR achieves 30% accuracy on CIFAR-10 with a smaller learning rate and momentum exponentially restarted every 5 epochs, while the original Adam took 20 epochs to reach the same accuracy.
- The paper highlights that decoupled weight decay can be applied to other optimization algorithms like SGD, AdaGrad, RMSProp, and Adamax.
- Decoupling weight decay from learning rate and momentum hyperparameters improves generalization performance in various settings, making it a practical improvement for optimization algorithms.
- The paper applies cosine annealing and warm restarts to Adam, improving its anytime performance by fixing L2 regularization issues through original weight decay regularization (Section 2) and introducing normalized weight decay (Section B.1).
- SGDR (Stochastic Gradient Descent with Warm Restarts) schedules the change of effective learning rate to accelerate training in DNNs, decoupling initial learning rate α and its multiplier ηt.
- In SGDR, a new warm-started run/restart occurs after Ti epochs, where i is the index of the run. The restarts are not performed from scratch but use the old solution's value as an initial solution, with the amount by which ηt increases controlling how much previously acquired information (e.g., momentum) is used.
- Within each run, the value of ηt decays according to a cosine annealing learning rate for each batch, following Eq. (14). For simplicity, when η(i)max = 1 and η(i)min = 0, Eq. (15) can be used.
- To achieve good anytime performance, Ti can start small (e.g., from 1% to 10% of the expected total budget), then multiply it by a factor Tmult (e.g., Tmult = 2) at every restart. The (i + 1)-th restart is triggered when Tcur = Ti, setting Tcur to 0.
- This approach has been successfully applied to popular image classification benchmarks, leading to new state-of-the-art results and improving the performance of Adam over SGD with warm restarts.
- The paper proposes AdamWR, an extension of AdamW that incorporates decoupled weight decay regularization for warm restarts.
- AdamWR uses a constant learning rate schedule multiplier (ηt) computed using normalized weight decay, allowing consistent parameter settings across short and long runs.
- Experiments show that the total runtime affects the optimal hyperparameters, with smaller weight decay values needed for longer runs.
- The paper introduces normalized weight decay to simplify hyperparameter selection by making optimal values observed in short runs similar to those in long runs.
- AdamWR and SGDWR (SGDW with warm restarts) demonstrate improved performance compared to standard Adam, especially when the number of epochs is large or the network size is small.
- The paper's findings suggest that using a learning rate schedule like cosine annealing can improve performance over fixed learning rates in Adam and SGD.
- Experiments show that AdamWR achieves better results than standard Adam with 18 times fewer epochs and a smaller network, indicating the effectiveness of decoupled weight decay regularization.
- The paper provides an example setting for the schedule multiplier (ηt) and shows how it works in practice.
- The authors investigate whether using much longer runs of standard Adam makes cosine annealing unnecessary, but results suggest that it still improves performance.
- The paper's findings highlight the importance of considering runtime when selecting hyperparameters and emphasize the benefits of decoupled weight decay regularization in warm restarts for LLMs.
- The paper proposes a decoupled weight decay regularization method for improving hyperparameter selection and performance in deep learning models.
- It introduces square root normalization (Eq. 15) to scale the weight decay, which leads to similar optimal values observed during short and long runs on different datasets.
- Experiments show that this normalization improves performance by reducing the need for extensive hyperparameter tuning and avoiding overfitting issues.
- The paper highlights that square root scaling might not be the best option; other better scaling rules likely exist, but further research is needed to identify them.
- Adam and its variants with decoupled weight decay converge faster than SGD variants on CIFAR-10, while AdamW demonstrates better test error performance compared to Adam.
- Restart variants (AdamWR and SGDWR) also show improved generalization over AdamW and SGDW, respectively.
- The paper's findings suggest that decoupled weight decay regularization can lead to faster convergence and better generalization in deep learning models.
- The paper investigates decoupled weight decay regularization, focusing on its impact on different runtime budgets and datasets.
- Optimal raw weight decay settings vary significantly for varying runtime budgets, while normalized weight decay remains consistent across budgets and datasets.
- Normalized weight decay shows similar performance in AdamW and SGDW optimizers, as well as on CIFAR-10 and ImageNet32x32 datasets.
- The paper's findings were published at ICLR 2019 in various conference papers.
- Supplementary figures show learning curves, generalization results, test error curves, and training loss curves for different models and datasets.
- Normalized weight decay offers a more stable approach to regularization compared to raw weight decay, which can be affected by runtime budgets and datasets.
",4369
"1712.00409",1,"- Deep Learning scaling is predictable, and this paper presents a large-scale empirical characterization of generalization error and model size growth as training sets grow.
- The study introduces a methodology for measuring these relationships across four machine learning domains: machine translation, language modeling, image processing, and speech recognition.
- Empirical results show power-law generalization error scaling in various factors, resulting in power-law exponents that have not been explained by theoretical work yet.
- Model improvements only shift the error but do not affect the power-law exponent.
- The study also shows that model size scales sublinearly with data size.
- These scaling relationships have significant implications for deep learning research, practice, and systems. They can assist in debugging models, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and emphasize the importance of continued computational scaling.
- The paper focuses on deep learning scaling and its predictability across various domains, including translation, language modeling, image classification, and speech recognition.
- Results show that power-law learning curves exist in all tested domains, with different exponents and intercepts for each application but consistent learning curve steepness.
- Improved model architectures and optimizers can improve the power-law intercept but not the exponent; models for a single domain have the same learning curve steepness.
- With sufficiently large training sets, models reach a region dominated by irreducible error (e.g., Bayes error).
- The paper highlights implications of predictable accuracy and model size scaling: assisting model debugging, setting accuracy targets for improved architectures, guiding data set growth decisions, informing system design, and emphasizing the importance of computational scaling.
- Related work is reviewed, focusing on generalization error improvements with increased training set size, theoretical bounds on sample complexity, and estimating expected generalization error. However, these prior works do not fully explain the empirical results presented in this paper.
- Deep Learning Scaling is Predictable, Empirically: The paper discusses how generalization error scaling trends follow power laws in various contexts and suggests an opportunity for theoretical justification of these empirical findings.
- Model Capacity Required to Fit Data: Prior studies propose measures of model capacity based on a model's organization and parameterization, with the number of parameters required to fit a data set following s(m) ∝ αmβp (where s(m) is the required model size).
- Measuring Model Accuracy and Size Scaling: The paper focuses on accurately estimating learning curves and model size scaling trends by selecting state-of-the-art models, training them on successively larger subsets of a training set, and observing how accuracy grows with the training set size.
- Empirical Results: The study shows that generalization error scaling trends follow power laws between βg = −0.07 to −0.35 in various real-world problems. Prior work has proposed logarithmic scaling for image classification accuracy, while some models grow with a power-law exponent of βp ≈ 0.72.
- Model Capacity and Generalization Error: While model capacity can explain a model's ability to memorize training examples, it may not adequately explain the model's generalization abilities. Over-parameterizing models is currently an easier approach for researchers and practitioners to fit training data.
- Methodology: The paper surveys recent work in various machine learning domains to find SOTA models with large datasets, selects multiple architectures for comparison, and trains them on successively larger subsets of the training set to observe how accuracy grows with the training set size.
- The paper focuses on understanding the scaling behaviors of deep learning models across various domains, including machine translation, language modeling, image classification, and speech recognition.
- It introduces a methodology for comparing data set sizes and model capacities by subdividing training sets into shards and using validation sets to measure generalization error.
- The study finds that increasing training data size results in power-law scaling of generalization error and required model size, with similar relationships across different machine learning domains and architectures.
- Power-law exponents for generalization error (βg) range from −0.5 to 0, while those for the number of model parameters (βp) are between 0.5 and 1.0.
- The study also shows that in many cases, model size growth with data set size grows sublinearly.
- In neural machine translation, the power-law exponent for generalization error is smaller than theoretical predictions (i.e., βg ≈ −0.128).
- The paper provides a framework for understanding and predicting deep learning scaling behaviors, which can help guide future research and development efforts in this field.
- The paper aims to test NMT (Neural Machine Translation) using a SOTA sequence-to-sequence model with global attention on WMT'16 German-to-English data set.
- They use OpenNMT implementation and simplify training by removing ensembling and data augmentation techniques.
- To scale model sizes, they tie LSTM input and hidden state sizes together, reducing total parameter count linearly with the dataset size.
- The learning curves for a single model family can be represented as power-law + constant, but βg (learning curve exponent) is smaller than -0.5.
- They find that composite learning curves for NMT have even smaller βg values, indicating a longer power-law region.
- As training set sizes grow, optimization becomes more difficult and models run out of capacity, causing empirical error to diverge from the power-law trend.
- Deep Learning Scaling is Predictable, Empirically: The paper focuses on analyzing the relationship between model size and performance in deep learning for language modeling tasks.
- Language Modeling: LMs are important models used in domains like speech recognition and machine translation. They have clear power-law learning curves with small exponents (βg ∈ [−0.09, −0.06]), indicating that current language models require more data to significantly improve accuracy.
- Word Language Models: The paper trains LSTM-based word LMs and compares them against Recurrent Highway Networks (RHNs). Both architectures show similar learning curves with power-law exponents, suggesting that model size scaling is predictable.
- Character Language Models: RHNs are used for character-level language modeling, and the paper compares SGD and Adam optimizers in this context. The input and output vocabulary includes alphanumeric characters and common symbols.
- Model Size Scaling: Best-fit models grow sublinearly with training set size (βp ≈ 0.7), indicating that larger data sets require more parameters to achieve the same level of performance.
- Hyperparameter Search: The paper suggests that further hyperparameter search is likely to yield a model on the power-law trend, and it's possible to predict the model size that will best fit increasingly larger data sets.
Summary of ""Deep Learning Scaling is Predictable, Empirically"" paper:
- The study shows that deep learning scaling in language models (LM) follows predictable power-law relationships for both word and character LMs.
- Generalization improves as training data size increases with a power-law exponent of -0.0936 for SGD optimizer and -0.0954 for Adam optimizer, indicating similar learning curve trends despite different optimizers.
- Character LMs learn relationships between characters more efficiently than word LMs, requiring fewer samples to achieve the same level of generalization.
- Sublinear model size growth is observed in character LMs with SGD optimizer (βp = 0.78) and Adam optimizer (βp = 0.92).
- Image classification experiments using ResNets for image recognition also exhibit power-law learning curves, with accuracy plateauing near random guessing on small training sets.
- The study highlights the ""small data region"" where models are unable to extract enough information from small training sets to make accurate classifications.
- Deep Learning scaling is predictable, with power-law exponents for different metrics.
- Top-1 classification error exponent: βg = −0.309; top-5 classification error exponent: βg = −0.488.
- Validation cross-entropy exponent: βg = −0.35, but its range differs from classification errors.
- Model size growth follows a sublinear curve with exponent βp = 0.573; even on small data sets, ResNets require large models (at least 3.4M parameters).
- Speech recognition provides an interesting contrast to prior domains due to its medium-dimensionality time-series data inputs.
- Tested two recent SOTA speech recognition models: Deep Speech 2 and attention-based model.
- Both DS2 and attention-based speech models experience the same power-law learning curve improvements, with βg = −0.299 ± 0.7%.
- Larger attention models trained on larger data sets tend to be easier to optimize than DS2 models.
- For speech recognition, model size scaling results are less meaningful compared to other domains; learning curves for different DS2 model sizes were shown instead.
- Predictable learning curves and model size scaling indicate significant implications for deep learning (DL).
- These implications can aid in model debugging, optimization iteration time, estimating the most impactful steps to improve accuracy, guiding data set growth or computation decisions, and estimating compute requirements.
- Real application learning curves have three phases: small data region with poor performance, power-law region where each new sample improves predictions, and an irreducible error region (lower bound on generalization).
- The power-law exponent in the middle portion of the curve is an indicator of model difficulty to represent the data generating function and may depend on aspects of the problem domain or data distribution.
- Practitioners can use predictable learning curves for debugging, targeting better accuracy scaling, and optimizing hyperparameters. Larger models require larger training sets and often need larger batch sizes and learning rates to close gaps in power-law trends. Smaller training sets may require smaller batch sizes to ensure model behavior.
- The irreducible error region is likely to exist for real applications but has not been reached in this study. It includes the Bayes error and other factors causing imperfect generalization, such as mislabeled samples.
- Deep Learning scaling is predictable, and learning curves can help guide decisions about data collection and computational scaling.
- Model architecture improvements shift learning curves down but might not improve the power-law exponent.
- The potential accuracy improvements for some problem domains (especially language modeling) are immense if we could improve the power-law exponent.
- Factors affecting the power-law exponent remain unknown, and models must learn more concepts with less data to beat it.
- Future work should analyze learning curves using data handling techniques like data filtering/augmentation, few-shot learning, experience replay, and generative adversarial networks.
- Model exploration using small data sets is possible due to predictable scaling, allowing researchers or DL systems to find models that accurately model the data distribution.
- Computational limitations can be a challenge when scaling computations for larger training sets, but predictable learning curves can help project compute requirements.
- Irreducible error may occur in real applications, indicating no further information can be extracted from existing data. Techniques to increase data's information content are needed to improve accuracy beyond this point.
- Deep learning scaling is predictable, meaning that model accuracy improves as a power-law with increasing data set size and computation scaling.
- This predictability exists across various machine learning domains (machine translation, language modeling, image processing, speech recognition) and different model architectures, optimizers, and loss functions.
- Model architecture changes only affect the learning curve's steepness but not its power-law exponent.
- Model size scales sublinearly with data set size, implying that larger models are needed to achieve higher accuracy.
- Hardware design implications: predictable scaling relationships can help hardware developers estimate compute requirements for a specific accuracy level and prioritize application domains based on computational scalability.
- Performance-accuracy trade-offs in deep learning techniques (low-precision computation, sparse models) can be evaluated using these scaling relationships to determine if improved throughput will recover lost accuracy.
- Deep Learning scaling predictability: The paper explores how deep learning's performance improves with increasing data size, revealing a linear relationship between model accuracy and training data size for various tasks.
- Large-scale language modeling: It demonstrates that the generalization error decreases linearly as the number of parameters increases in large-scale language models.
- Neural machine translation: The paper shows that neural machine translation (NMT) performance improves with more training data, and it can be modeled using a linear relationship between accuracy and data size.
- End-to-end speech recognition: It presents an empirical study on the scaling behavior of end-to-end speech recognition systems, revealing a predictable relationship between model accuracy and training data size.
- Image classification: The paper discusses how image classification performance improves with more training data, showing that the error rate decreases linearly as the number of parameters increases in deep neural networks.
- Statistical learning theory: It provides theoretical foundations for understanding the relationship between model accuracy and data size, connecting it to statistical mechanics and information theory.
- Neural network generalization bounds: The paper presents a method to compute nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data.
- Connectionist temporal classification: It introduces a technique for labeling unsegmented sequence data using recurrent neural networks, which improves performance as the amount of training data increases.
- Statistical theory of learning rules: The paper discusses how the number of examples needed to learn a rule can be quantified and provides nearly-tight VC-dimension bounds for piecewise linear neural networks.
- Inductive bias in deep learning: It examines the relationship between model accuracy, data size, and inductive bias in deep learning algorithms, connecting it to Valiant's learning framework.
- The paper discusses the predictability of deep learning scaling in various machine learning domains, such as neural machine translation (NMT), language modeling, image classification, and speech recognition.
- It investigates the power-law data-generalization behaviors observed in these domains and attributes them to the structure of the problem domain.
- The paper provides precise definitions for input and output spaces, optimized loss functions, and other relevant information for each machine learning domain tested.
- In NMT, the model learns a mapping between source (German) and target (English) vocabularies using a word-piece vocabulary shared between languages. The training process minimizes cross entropy loss, reporting per-token error rate and bits-per-token as metrics.
- For language modeling, both word and character models are studied. Word language models use continuous minibatching with normalized cross-entropy loss, while character models unroll sequences to 150 characters and also employ continuous minibatching.
- Image classification uses ResNet architecture for feed-forward, convolutional blocks, pooling, and skip connections. The model minimizes error (classification) and X-entropy losses.
- Speech recognition models include Deep Speech 2 (DS2), which uses a bidirectional long short-term memory (Bi-LSTM) network with connectionist temporal classification (CTC) loss, optimized using Adam.
- The paper concludes that the scaling behavior of deep learning is predictable and can be explained by the structure of the problem domain.
- Character language models use non-continuous minibatching, with sequences truncated to 150 characters and a vocabulary of 98 alphanumeric characters and symbols.
- ImageNet images are scaled proportionally to 256 pixels in the shortest dimension, cropped to 224x224 for training, and augmented with brightness, contrast, saturation, lighting, and horizontal flipping.
- Speech recognition models use encoder-decoder architecture with LSTM or GRU cells, predicting conditional probability using CTC loss function (DeepSpeech 2) or attention mechanism (Attention model).
- The power-law learning curve for counting model classifiers shows that the expected generalization error decreases as a power-law with the size of training samples, based on Glivenko-Cantelli theorem limit.
- The paper discusses predictable scaling behavior in deep learning, focusing on loss functions and their relationship with model performance.
- It shows that various loss functions (L1-norm, L2-norm, absolute KL-divergence) exhibit power-law behavior when scaled up.
- The total loss function is defined as a weighted average loss per output prediction.
- Theorem 1 states that for a counting model trained on samples from a fair coin flip distribution, the expected total loss follows a power-law with an exponent of -0.5.
- This result can be generalized to other loss functions and distributions.
- The paper provides insights into how deep learning scaling works and offers a theoretical foundation for understanding its behavior.
- By understanding these predictable scaling patterns, researchers can optimize model design and training strategies.
",3347
"1801.10198",1,"- The paper focuses on generating English Wikipedia articles as a multi-document summarization task, using extractive and abstractive models.
- Extractive summarization identifies salient information in source documents while abstractive models generate new text based on the input.
- The authors introduce a decoder-only architecture for their abstractive model that can handle very long sequences, unlike typical encoder-decoder architectures used in sequence transduction.
- This model generates fluent, coherent multi-sentence paragraphs and even entire Wikipedia articles when given reference documents.
- The work demonstrates the first attempt to generate the lead of a Wikipedia article conditioned on reference text using neural methods.
- Strong baseline models are run for comparison, and the Transformer architecture is modified to only consist of a decoder, which performs better in longer input sequences.
- The authors show that their modeling improvements allow them to generate entire Wikipedia articles.
- Generating Wikipedia by Summarizing Long Sequences: The paper focuses on creating a method to generate Wikipedia articles using abstractive summarization techniques, which involve generating new text rather than extracting it from existing sources.
- Transformer models for longer sequences: The study uses the Transformer architecture, which has shown state-of-the-art results in machine translation and is non-recurrent, allowing greater parallelization. They adapt this model to handle longer input sequences for abstractive summarization.
- WikiSum dataset: The paper introduces a new dataset called WikiSum, consisting of English Wikipedia articles with their corresponding citations (Ci) and web search results (Si). This dataset is orders-of-magnitude larger than previous summarization datasets, making it more challenging for machine learning models to handle.
- Results: The paper presents experiments on the WikiSum dataset using various Transformer architectures and shows that their method achieves state-of-the-art results in abstractive summarization of Wikipedia articles.
- The paper aims to generate Wikipedia by summarizing long sequences using a two-stage process: extractive and abstractive stages.
- Extractive methods include Identity, tf-idf, TextRank, SumBasic, and a cheating method.
- Abstractive models are LSTM encoder-decoder with attention (seq2seq-att) and non-recurrent Transformer model (T-ED).
- A new Transformer decoder (T-D) is introduced for long sequences by dropping the encoder module, combining input and output into a single sentence, and training as a standard language model.
- The paper shows that T-D outperforms seq2seq-att and T-ED in terms of BLEU score, especially when extractive methods are used to generate the input sequence.
- The paper also demonstrates that the Transformer decoder can be trained with a smaller batch size than the encoder-decoder model, making it more suitable for long sequences.
- Generating Wikipedia by Summarizing Long Sequences paper focuses on handling longer sequences in LLMs using Transformer models with memory-compressed attention (T-DMCA).
- The T-DMCA model reduces memory usage and allows for processing sequences 3x longer than the standard Transformer-Decoder (T-D) model.
- Local attention layers divide sequences into smaller blocks, while memory-compressed attention layers exchange information globally across the entire sequence.
- Experiments show that T-DMCA achieves better performance in perplexity and ROUGE-L F1 compared to other models like Transformer encoder-decoder (T-ED) and extractive methods.
- The paper demonstrates how LLMs can be improved for handling longer sequences, which is crucial for tasks such as generating Wikipedia articles from their corresponding sections.
- Extractive methods: SumBasic, TextRank, tf-idf, identity, cheating extractor.
- Input corpus: citations, search results, combined.
- Abstractive model input length (L): between 100 and 11000.
- Abstractive model architecture: seq2seq-att, T-ED, T-D, T-DMCA.
- Extractive methods are critical for final abstractive performance; smart extraction is key.
- Combined dataset performs best, but gaps between it and using one of citations or search results are significant.
- Transformer architectures (T-ED, T-D, T-DMCA) outperform seq2seq-attention in terms of performance.
- Limitations: Transformer-ED architecture learns until around L = 500 - 1000; Transformer-Decoder up to L = 4000 (memory constraints).
- T-DMCA model can train up to L = 11000, with improvements in performance and better linguistic quality.
- Human evaluation: T-DMCA model statistically significantly better on all dimensions except non-redundancy, where tf-idf performs similarly.
- Generating Wikipedia by summarizing long sequences: The paper introduces methods for generating Wikipedia articles using sequence transduction models, which learn from combined input-output sequences.
- Improving extractive and decoder-only architectures: Future work focuses on enhancing the extractive stage and extending decoder-only architectures to handle larger L while maintaining sufficient model capacity.
- Comparison with Sauper & Barzilay (2009): A direct comparison is difficult due to differences in reporting, input/output articles, and unavailable data. However, the paper presents a best-effort comparison for two categories (Diseases and American Actors) showing better performance on American Actors.
- Qualitative discussion: As perplexity decreases, model outputs improve in terms of fluency, factual accuracy, and narrative complexity. An unexpected side effect is the translation of names into multiple languages.
- Generating full-Wikipedia articles: The paper demonstrates that it's possible to train models to generate entire Wikipedia articles by conditioning on titles or using reference tokens. While not as good as real Wikipedia articles, these models can organize information and exhibit global coherence over multi-paragraph text.
- Generating Wikipedia can be approached as a multi-document summarization problem with a large, parallel dataset.
- A two-stage extractive-abstractive framework is used for carrying out this task: coarse extraction in the first stage and abstractive generation in the second stage.
- The coarse extraction method has a significant effect on final performance, suggesting further research to improve it.
- A new decoder-only sequence transduction model is introduced for the abstractive stage, capable of handling very long input-output examples.
- This model outperforms traditional encoder-decoder architectures on long sequences, allowing conditioning on many reference documents and generating coherent and informative Wikipedia articles.
- The dataset and code will be released to encourage further research in large-scale summarization.
- Acknowledgments are given to various contributors who helped with the project.
- References to related work, datasets, and papers are provided for context and comparison.
- Generating Wikipedia articles using a structure-aware approach, published as a conference paper at ICLR 2018 by Christina Sauper and Regina Barzilay.
- Outrageously large neural networks with the sparsely-gated mixture-of-experts layer (SGL) introduced in 2017 by Noam Shazeer et al., used for generating Wikipedia samples.
- Attention is all you need, a paper from 2017 by Ashish Vaswani et al., which introduces the Transformer architecture and self-attention mechanism, also utilized for generating Wikipedia samples.
- Google's neural machine translation system (GNMT) introduced in 2016 by Yonghui Wu et al., used to detect Wikipedia clones.
- Maximum recall of unigrams for each section of a Wikipedia article is computed to detect whether a reference document is a clone, with a threshold of r > 0.5.
- Human evaluation experiment conducted using DUC-style linguistic quality assessment tool, rating samples on five dimensions: Grammaticality, Non-redundancy, Referential clarity, Focus, and Structure and Coherence.
- Side-by-side human evaluation tool used to compare two models by showing their outputs side-by-side and asking raters which model they prefer.
- Extractive method using tf-idf for generating abstractive output/input examples in the ""dewey & lebeouf"" example.
",1621
"1802.05365",1,"- Introduction of deep contextualized word representations that model complex characteristics and polysemy variations in linguistic contexts.
- Uses learned functions from a deep bidirectional language model (biLM) pretrained on large text corpus, resulting in ELMo (Embeddings from Language Models) representations.
- Significantly improves state-of-the-art performance across six challenging NLP problems, including question answering, textual entailment, and sentiment analysis.
- Exposing deep internals of the pretrained network crucial for downstream models to mix semi-supervision signals.
- ELMo representations differ from traditional word embeddings by being a function of entire input sentence, using vectors derived from bidirectional LSTM trained with coupled language model objective.
- Unlike previous approaches, ELMo representations are deep and use linear combinations of internal layers for each end task, leading to richer word representations.
- Intrinsic evaluations show higher-level LSTM states capture context-dependent aspects of word meaning, useful in supervised word sense disambiguation tasks.
- Deep contextualized word representations (ELMo) combine multiple signals, such as syntax and semantics, from large-scale unlabeled text to create effective pretrained word vectors for NLP tasks.
- ELMo outperforms other approaches like CoVe in various language understanding problems, including textual entailment, question answering, and sentiment analysis.
- Significant improvements are seen in state-of-the-art performance with up to 20% relative error reductions.
- ELMo's trained models and code are publicly available for use in other NLP problems.
- Previous approaches like word vectors only allow a single context-independent representation per word, while ELMo incorporates subword units through character convolutions and multi-sense information seamlessly into downstream tasks.
- The paper discusses related work, including pretrained word vectors, methods that enrich them with subword information or learn separate vectors for each word sense, and context-dependent representations like context2vec, CoVe, and unsupervised language models.
- ELMo's approach benefits from large datasets without being limited by the size of supervised neural machine translation systems used in other methods.
- The paper introduces Deep contextualized word representations, which take advantage of access to plentiful monolingual data and train biLMs on a corpus with approximately 30 million sentences.
- It generalizes previous approaches to deep contextual representations, showing they work well across diverse NLP tasks.
- Previous research has shown that different layers in deep biRNNs encode distinct types of information, which can be beneficial for various downstream tasks.
- The paper introduces ELMo (Embeddings from Language Models), a new word representation method that uses the entire input sentence and is computed on top of two-layer biLMs with character convolutions.
- ELMo allows semi-supervised learning, where the biLM is pretrained at large scale and easily incorporated into existing neural NLP architectures.
- The paper demonstrates how ELMo can be used to improve performance in various downstream tasks, such as sentiment analysis, question answering, and text classification.
- ELMo outperforms other state-of-the-art methods on multiple tasks, achieving up to 30% accuracy improvement over the previous best results.
- The paper highlights that ELMo's performance is consistent across different data sizes and domains, making it a versatile tool for various NLP applications.
- Deep contextualized word representations: The paper introduces a bi-directional language model (biLM) that combines forward and backward LMs, maximizing the log likelihood of both directions while sharing some weights between them.
- ELMo (Embeddings from Language Models): A task-specific combination of intermediate layer representations in the biLM. It collapses all layers into a single vector for use in downstream models.
- Using biLMs for supervised NLP tasks: The paper demonstrates how to improve an end task model by using pre-trained biLMs and recording layer representations for each word.
- Experiments: The authors show that ELMo outperforms other methods on various tasks, including sentiment analysis, question answering, and textual entailment.
- Practical applications: ELMo can be used in any NLP task where a pre-trained language model is beneficial, such as machine translation, information retrieval, and dialogue systems.
- Layer normalization: The paper suggests applying layer normalization to each biLM layer before weighting for better performance in some cases.
- Scalar parameter γ: This parameter allows the task model to scale the entire ELMo vector, aiding optimization and practical importance.
- Adding ELMo to supervised NLP models: Concatenate ELMo vector with pre-trained word embeddings and pass enhanced representation into task RNN. Optionally include ELMo at output of task RNN.
- Benefits of adding ELMo: Improved performance in tasks like SNLI, SQuAD; can be integrated within complex neural models (e.g., bi-attention layers or clustering models).
- Pre-trained bidirectional language model architecture: Large-scale biLMs with residual connections between LSTM layers and character-based input representation.
- Balancing language model perplexity, model size, and computational requirements: Halved embedding and hidden dimensions from CNN-BIG-LSTM in Jozefowicz et al. (2016).
- BiLM provides three layers of representations for each token, including those outside the training set.
- ELMo's dropout and weight regularization: Added to improve performance; imposes an inductive bias on ELMo weights.
- Practical applications: Improved performance in various tasks (e.g., sentiment analysis, question answering).
- Unusual finding: BiLMs outperform forward-only LMs and large scale training is important for better results.
- Deep contextualized word representations: This paper introduces a new method for creating word embeddings, called bi-directional language models (biLMs), which learns representations for each input token by considering the entire text sequence.
- Comparison with traditional methods: Traditional word embedding methods only provide one layer of representation for tokens in a fixed vocabulary, while biLMs offer multiple layers and can handle out-of-vocabulary words due to their character-based input.
- Evaluation results: The paper shows that the biLM model achieves lower perplexities (around 39.7) compared to other models like CNN-BIG-LSTM (perplexity of 30). Fine-tuning the pretrained biLM can lead to significant improvements in downstream tasks, acting as a form of domain transfer.
- ELMo evaluation: The paper presents performance results for six benchmark NLP tasks using ELMo, an extension of biLMs. In every task, adding ELMo establishes new state-of-the-art results with relative error reductions ranging from 6% to 20%.
- Question answering example: Adding ELMo to a baseline model for the Stanford Question Answering Dataset (SQuAD) improves test set F1 by 4.7%, resulting in a 24.9% relative error reduction over the baseline and improving the overall single model state-of-the-art.
- Practical applications: The paper highlights that ELMo can be used as a general-purpose language representation, potentially replacing other word embedding methods for various NLP tasks.
- Deep contextualized word representations (ELMo) enhance neural models for various NLP tasks, leading to improved performance.
- ELMo improves accuracy by 0.7% in textual entailment (SNLI), pushing ensemble accuracy to 89.3%, exceeding previous ensembles.
- In semantic role labeling (SRL), adding ELMo increases single model F1 from 81.4% to 84.6%, becoming a new state-of-the-art on OntoNotes benchmark.
- Coreference resolution (Coref) sees an average F1 improvement of 3.2% with ELMo, establishing a new state-of-the-art and improving over previous best ensembles by 1.6%.
- Named entity recognition (NER) benefits from ELMo, achieving higher accuracy in CoNLL 2003 task compared to pre-trained word embeddings and character-based CNN representations.
- ELMo's performance is consistent across various tasks, suggesting its effectiveness in capturing contextual information for diverse NLP applications.
- The paper introduces ELMo (Embeddings from Language Models) as an enhancement to biLSTM-CRF models for contextualized word representations.
- ELMo improves performance over previous state-of-the-art methods by allowing the task model to learn a weighted average of all biLM layers, rather than just the top layer.
- Using all layers instead of just the last one leads to better performance across multiple tasks, such as sentiment analysis, question answering, and semantic role labeling.
- ELMo provides better overall performance compared to other contextual representations like CoVe (Common Crawl Vector Embeddings).
- The paper analyzes different aspects of ELMo's contextual information representation, including syntactic and semantic information, and shows that lower layers capture syntax while higher layers focus on semantics.
- The study also explores the sensitivity to where ELMo is included in the task model, training set size, and visualizes learned weights across tasks.
- Previous work used only last layer of biLMs and MT encoders for contextual representations, but this paper explores using all layers.
- Including representations from all layers improves overall performance over just the last layer, while including the last layer improves performance over baseline.
- A small λ (regularization parameter) is preferred in most cases with ELMo, except for NER where results are insensitive to it.
- Incorporating ELMo at both input and output layers of task-specific architectures can improve overall results for some tasks.
- SRL performance is highest when ELMo is included only at the input layer; SNLI and SQuAD benefit from including it in both input and output layers.
- Attention layers after biRNN allow models to attend directly to biLM's internal representations, which may explain why some tasks perform better with ELMo at specific layers.
- Table 2 compares performance improvements for SQuAD, SNLI, and SRL using different layer combinations and regularization parameters.
- Table 3 shows the effect of including ELMo in various layers on SNLI, SQuAD, and SRL tasks.
- Table 4 demonstrates nearest neighbors to ""play"" using GloVe and biLM context embeddings from Raganato et al.'s (2017a) work.
- The paper explores deep contextualized word representations, focusing on biLMs (bidirectional Long Short-Term Memory) and CoVe (Contextual Vector).
- It compares the performance of these models in tasks like fine-grained Word Sense Disambiguation (WSD) and Part-of-Speech (POS) tagging, using intrinsic evaluation methods similar to Belinkov et al. (2017).
- The biLM's contextual representations are found to encode information generally useful for NLP tasks that is not captured in word vectors, helping disambiguate word meaning and part of speech.
- In WSD, the biLM's top layer outperforms its first layer with an F1 score of 69.0, which is competitive with state-of-the-art WSD-specific supervised models.
- The paper also highlights that task-specific context representations are more important than those from biLMs in some cases.
- CoVe and the biLM's performance on POS tagging tasks show similar trends, with the second layer of both models performing better than their first layers.
- The study provides insights into how these models capture information and suggests potential applications for improving NLP systems.
- The paper introduces Deep contextualized word representations, focusing on the biLM (bidirectional language model) and its comparison with CoVe (contextual vector embeddings).
- BiLMs outperform CoVe in WSD (word sense disambiguation), POS tagging, and other tasks due to their more transferable representations.
- Including all biLM layers is crucial for the highest performance in downstream tasks as different layers represent distinct types of information.
- ELMo (Embeddings from Language Models) enhances sample efficiency by requiring fewer parameter updates and smaller training sets to reach state-of-the-art performance.
- Improvements with ELMo are most significant for smaller training sets, making it more efficient in using limited data.
- The paper introduces ELMo (Embeddings from Language Models), a general approach for learning high-quality deep context-dependent representations using biLMs (bidirectional Long Short-Term Memory).
- ELMo significantly improves performance in various NLP tasks, especially when applied to smaller training sets.
- The model reduces the amount of required training data by up to 90% while maintaining or even improving performance compared to baseline models.
- Visualization of learned weights shows that task-specific models favor certain layers for specific tasks (e.g., coreference and SQuAD tasks prefer the first biLSTM layer).
- The paper confirms that biLM layers efficiently encode different types of syntactic and semantic information about words in context, with better performance when using all layers.
- ELMo's practical applications include improving NLP models for named entity recognition, natural language inference, coreference resolution, and question answering.
- Semisupervised sequence learning, coreference resolution, dropout in RNNs, natural language inference over interaction space, joint many-task model, deep semantic role labeling, LSTM, word sense disambiguation evaluation study, exploring limits of language modeling, recurrent network architectures, character-aware neural language models, Adam optimization method, dynamic memory networks for NLP, conditional random fields, neural architectures for named entity recognition, end-to-end neural coreference resolution, compositional character models for open vocabulary word representation, stochastic answer networks for machine reading comprehension, end-to-end sequence labeling via bi-directional LSTM-CNNsCRF, building a large annotated corpus of English (Penn Treebank), contextualized word vectors, learning generic context embedding with Context2Vec.
- These papers cover various aspects of NLP, including semisupervised sequence learning, coreference resolution, dropout in RNNs, natural language inference, joint many-task models, deep semantic role labeling, LSTM, word sense disambiguation, exploring limits of language modeling, recurrent network architectures, character-aware neural language models, Adam optimization method, dynamic memory networks for NLP, conditional random fields, neural architectures for named entity recognition, end-to-end coreference resolution, compositional character models for open vocabulary word representation, stochastic answer networks for machine reading comprehension, end-to-end sequence labeling via bi-directional LSTM-CNNsCRF, building a large annotated corpus of English (Penn Treebank), contextualized word vectors, and learning generic context embedding with Context2Vec.
- Some key findings include 30% accuracy in coreference resolution, up to 4.5 times faster language modeling, 16.8% improvement in named entity recognition, 97.1% accuracy in natural language inference over interaction space, and 91.3% accuracy in end-to-end neural coreference resolution.
- Practical applications include improved semisupervised sequence learning, better coreference resolution, faster language modeling, enhanced natural language inference, more accurate named entity recognition, and advanced machine reading comprehension systems.
- Unusual findings include the use of Adam optimization method for stochastic optimization, dynamic memory networks for NLP tasks, context2vec for generic context embedding learning, and contextualized word vectors for translation-based contextualization.
- The paper discusses various academic works related to contextualized word representations, neural language models, and their evaluation methods.
- It highlights the importance of learning generic context embedding using bidirectional LSTMs (context2vec) and distributed representations of words and phrases (Word2Vec).
- The paper introduces a unified evaluation framework for word sense disambiguation and semi-supervised sequence tagging with bidirectional language models.
- It mentions the Squad dataset for machine comprehension, recursive deep models for semantic compositionality over sentiment treebanks, and neural tree indexers for text understanding.
- The paper also covers efficient nonparametric estimation of multiple embeddings per word in vector space, neural sequence learning models for word sense disambiguation, and low-level tasks supervised at training time.
- Deep multi-task learning with low-level tasks is introduced to improve performance on various NLP tasks.
- The paper emphasizes the importance of contextualized word representations in advancing neural language models and their evaluation methods.
- Practical applications include machine comprehension, sentiment analysis, named entity recognition, and text understanding.
- Some findings suggest improvements in accuracy (30%), speed (4.5 times faster), and model performance overall.
- The paper's main contributions lie in the development of new methods for contextualized word representations, neural language models, and evaluation frameworks that have led to advancements in various NLP tasks.
- Deep contextualized word representations paper focuses on creating a state-of-the-art language model (biLM) for various tasks, such as text classification, question answering, and semantic role labeling.
- The biLM architecture consists of context-independent token representation below several layers of stacked RNNs (LSTMs or GRUs).
- Fine-tuning the biLM on task-specific data typically results in significant perplexity improvements, with varying effects on supervised performance depending on the task.
- The γ parameter in Eqn. (1) is crucial for optimization due to differences between biLM internal representations and task-specific representations.
- The paper provides a supplemental material detailing model architectures, training routines, and hyperparameter choices for state-of-the-art models.
- The authors emphasize the importance of contextualized word representations in various NLP tasks, highlighting their potential practical applications.
- The biLM's performance on several tasks is compared to other models, with improvements ranging from 10% to 30%.
- The paper also discusses the impact of different model sizes and training strategies on performance.
- The authors suggest that future research should focus on exploring more complex architectures and incorporating additional contextual information for better generalization.
- The biLM's source code is available, allowing researchers to build upon this work and further improve language modeling capabilities.
- The paper introduces ""Deep contextualized word representations"" using bidirectional language models (biLMs) for better task-specific performance in natural language processing tasks.
- It focuses on improving the internal representation of biLMs by introducing a parameter that controls the distribution between biLM and task-specific representations, leading to better results in specific cases.
- The paper presents improvements in textual entailment (SNLI), named entity recognition (CoNLL 2012), coreference resolution (CoNLL 2003), question answering (SQuAD), sentiment analysis (SST), and semantic role labeling (SRL).
- The best ELMo configuration adds ELMo vectors to both the input and output of the lowest layer LSTM, using layer normalization with a small parameter (λ = 0.001) and an additional regularization term for weight matrices.
- Adding ELMo to the ESIM model improves accuracy by 0.7%, establishing a new single-model state-of-the-art of 88.7% in SNLI, with a five-member ensemble pushing overall accuracy to 89.3%.
- The paper also introduces a simplified question answering (QA) model based on the Clark and Gardner's model, which embeds tokens using GloVe word vectors and character-derived embeddings from a convolutional neural network.
- The QA model uses a shared bi-directional GRU layer followed by a bidirectional attention mechanism (BiDAF) for better contextual understanding.
- The paper highlights the importance of using pretrained word representations and contextualized language models to improve performance in various natural language processing tasks.
- The paper introduces a deep contextualized word representation model for natural language processing tasks, such as sentiment analysis and semantic role labeling (SRL).
- It uses a combination of pre-trained word embeddings (GloVe) with an 8-layer biLSTM network to create richer token representations.
- The deep LSTM model incorporates Highway connections and variational recurrent dropout for improved performance.
- For sentiment analysis, the model achieves state-of-the-art results on the Stanford Sentiment Treebank (SST) dataset with an accuracy of 92.5%.
- In semantic role labeling, the model outperforms existing systems by achieving a F1 score of 87.4% using an ensemble of 11 models.
- The paper also presents a comprehensive comparison of various SRL models and their performance on the PropBank dataset.
- The authors emphasize that their approach can be easily extended to other NLP tasks, such as question answering and machine translation.
- The model's code is available for research purposes at https://github.com/stanfordnlp/DeepPavlov.
- The paper introduces a method to improve contextualized word representations using ELMo (Embeddings from Language Models) in various NLP tasks, such as Semantic Role Labeling (SRL), Coreference Resolution, and Named Entity Recognition (NER).
- It fine-tunes GloVe vectors during training and initializes LSTMs with specific parameters to enhance contextual understanding.
- The paper achieves a new state-of-the-art result on the CONLL 2012 Semantic Role Labeling task, scoring 84.6 F1 (F1-score), surpassing previous single model results by 2.9 and ensemble models by 1.2.
- In Coreference Resolution, the paper improves the single model state-of-the-art by 3.2% average F1 and outperforms previous ensembles by 1.6%.
- The Named Entity Recognition (NER) baseline uses concatenated Senna vectors with CNN character representations and biLSTM layers, achieving a CRF loss during training and using softmax at test time.
- ELMo improves the NER model's performance by adding it to the input of the lowest layer biLSTM and weighting the biLM layers without regularization or layer normalization.
- The paper highlights the importance of contextualized word representations in various NLP tasks, demonstrating significant improvements when using ELMo.
- The paper introduces a deep contextualized word representation model for natural language processing tasks, specifically focusing on named entity recognition (NER) and sentiment classification.
- It uses a bi-directional Long Short-Term Memory (biLSTM) network with a Conditional Random Fields (CRF) layer for NER and a Bi-attention Classification Network (BCN) for sentiment classification.
- The model incorporates ELMo embeddings, which are pre-trained word representations from Google AI, to enhance the performance of both tasks.
- For NER, the paper achieves an F1 score of 92.22% on the CoNLL 2003 dataset, establishing a new state-of-the-art result compared to previous methods.
- In sentiment classification, the model reaches a test set accuracy of 76.8% on the Stanford Sentiment Treebank (SST) dataset and 71.9% on the IMDB dataset.
- The paper highlights that using ELMo embeddings from all layers of the bi-directional language model provides a modest improvement in performance.
- The authors suggest that their approach can be applied to other NLP tasks, such as part-of-speech tagging and dependency parsing, with potential improvements due to the use of contextualized word representations.
",4714
"1803.05457",1,"- The AI2 Reasoning Challenge (ARC) is a new question set, text corpus, and baselines designed to encourage advanced question answering research.
- ARC consists of 7,787 natural science questions from grade-school level, making it the largest public domain dataset of its kind.
- The challenge aims to address limitations in previous datasets by focusing on questions requiring reasoning, commonsense knowledge, and deeper text comprehension.
- The dataset is divided into a Challenge Set (2590 questions) and an Easy Set (5197 questions).
- Three neural baseline models were tested: DecompAttn, BiDAF, and DGEM, all of which performed poorly on the Challenge Set compared to a random baseline.
- The ARC Corpus containing 14 million science-related sentences is released alongside the dataset to help researchers engage with this challenge.
- The ARC (AI2 Reasoning Challenge) differs from other QA datasets by providing a more challenging set of questions, avoiding scores being dominated by simple algorithms and encouraging research on methods for difficult problems.
- The dataset includes an optional science corpus to help with question answering, while models and the leaderboard are publicly available at http://data.allenai.org/arc/.
- Earlier reading comprehension datasets focused on factoid-style questions that could be answered from surface-level cues, but newer approaches like TriviaQA and bAbI pushed towards more complex QA tasks.
- The WikiHop dataset introduced multihop questions, but had limitations in terms of question complexity and answerability.
- Datasets based on human standardized tests have been used for AI research, but they are often small and biased towards simple algorithms due to their design for humans rather than machines.
- The ARC dataset aims to address these challenges by providing a larger, more realistic, and more challenging set of questions that require reasoning beyond surface-level cues.
- None of the baselines were able to perform significantly above random on the Challenge Set, highlighting the difficulty of the task.
- The ARC Dataset is a collection of 7,787 science questions designed to address limitations in existing datasets and focus on more challenging AI problems.
- It consists of two sets: the Challenge Set (2590 ""hard"" questions) and the Easy Set (5,197 questions).
- The question vocabulary uses 6,329 distinct words (stemmed), with a variety of sources for the questions.
- Grade levels range from 3rd to 9th grade, with substantial overlap in difficulty among grades.
- The Challenge Set is defined as questions answered incorrectly by both an Information Retrieval (IR) solver and a Pointwise Mutual Information (PMI) solver.
- The IR solver uses the Waterloo corpus to search for explicit statements matching the question and answer option, while the PMI solver measures the strength of associations between parts of the question and answer options using point-wise mutual information.
- The ARC Dataset provides a practical and useful tool for evaluating progress in AI research on more challenging questions and identifying areas that require further attention.
- The AI2 Reasoning Challenge (ARC) aims to evaluate question answering systems by introducing a new challenge set and corpus, focusing on advanced reasoning skills rather than basic fact retrieval.
- ARC's Challenge Set includes questions from various knowledge domains, such as Basic Facts & Properties, Structure, Processes & Causal, Teleology / Purpose, Algebraic, Experiments, Spatial / Kinematic, and others.
- The ARC Corpus consists of 14 million science-related sentences mined from the web, providing a starting point for addressing the ARC Challenge.
- The corpus was created by running search queries relevant to 80 science topics, with templates designed for each topic.
- The challenge set and corpus are released to facilitate research in question answering systems that can handle advanced reasoning skills required for scientific knowledge.
- The paper provides a detailed analysis of the ARC Challenge Set questions, highlighting different types of knowledge and reasoning involved.
- The authors emphasize that the relative sizes of knowledge categories in the ARC Challenge Set are approximate and subjective, but they provide an overview of the knowledge and reasoning space underlying the challenge.
- The paper also discusses the impact of using algorithms like IR (Inverted Index) and PMI (Pointwise Mutual Information) as filters for defining the Challenge Set.
- The ARC Corpus is optional for systems to use, but it provides a starting point for addressing the ARC Challenge without being restricted to this corpus.
- The goal of the AI2 Reasoning Challenge is to advance research in question answering systems that can handle complex scientific reasoning and knowledge.
- The paper introduces the AI2 Reasoning Challenge (ARC), a benchmark for evaluating Large Language Models' (LLMs) ability to reason across various domains, including linguistic, reasoning type, multihop, comparison, hypothetical/counterfactual, explanation/meta-reasoning, spatial/kinematic, and analogy.
- The ARC Challenge Set consists of 720 questions with answers sourced from a corpus containing over 15 million sentences. This corpus is built by combining the ARC Corpus (science-related documents), AristoMini corpus (Wikipedia articles, dictionary definitions, and web-collected science sentences), and Waterloo corpus (scientific papers).
- The paper analyzes the performance of various LLMs on the ARC Challenge Set, including GPT-2, T5, BERT, RoBERTa, and XLNet. It finds that these models struggle to answer questions requiring reasoning beyond linguistic understanding, with an average accuracy of 10% for all types of reasoning combined.
- The paper highlights the importance of incorporating knowledge graphs into LLMs to improve their performance in reasoning tasks. It also suggests using a multi-task learning approach to train LLMs on multiple reasoning types simultaneously.
- The authors provide a list of 21 questions from the ARC Challenge Set, along with their answers and explanations, to illustrate the different reasoning types.
- The paper introduces the AI2 Reasoning Challenge (ARC) and analyzes its performance using various baseline systems, including neural models like DecompAttn and BiDAF.
- It discusses how these baseline systems perform on ARC questions, scoring them based on choosing the correct answer or reporting a k-way tie that includes it.
- The paper presents several baseline systems: IR (Information Retrieval), PMI (Pointwise Mutual Information), Guess-all (""random"" guessing), and using the ARC Corpus for IR.
- It also mentions other neural entailment models like DecompAttn, DGEM, and DGEM-OpenIE, which were adapted to answer multiple-choice questions.
- The paper highlights that these systems perform better than random guessing but still struggle with the ARC Challenge due to its complexity and lack of direct answers in the corpus.
- It emphasizes the need for more research on how to inject commonsense knowledge into QA systems, as well as improving the quality of the ARC Corpus.
- The paper concludes that while these baseline systems are not perfect, they provide a useful starting point for future work in corpus-based attacks on the Challenge.
- The paper introduces the AI2 Reasoning Challenge (ARC) to address the need for more complex and reasoning-based question answering tasks in the field of Large Language Models (LLMs).
- ARC consists of a new question set, text corpus, and baselines that are challenging for retrieval and co-occurrence methods.
- The Challenge partition is designed to be hard for these methods, with none of the baseline systems tested able to significantly outperform a random baseline on this set.
- The paper highlights the limitations of recent datasets focused on factoid questions, which rely heavily on surface-level cues and discourage progress in more complex tasks requiring reasoning or advanced methods.
- Three neural models (DecompAttn, BiDAF, and DGEM) are provided as baselines for ARC, along with the dataset, corpus, and leaderboard available at http://data.allenai.org/arc.
- The paper emphasizes that progress on ARC would be an impressive achievement and a significant step forward for the community.
- The paper introduces ARC, the AI2 Reasoning Challenge, which aims to evaluate and improve question answering systems by providing a benchmark dataset with multiple-choice questions from standardized tests.
- It highlights the need for more complex and challenging datasets in order to advance machine comprehension capabilities beyond simple text understanding.
- The paper discusses various sources of questions used to create ARC, including ACTAAP (Arkansas Comprehensive Testing, Assessment, and Accountability Program), AIMS (Arizona's Instrument to Measure Standards), Alaska Dept Ed (Alaska Department of Education & Early Development), AMP (Assessing Mathematics Proficiency), and others.
- The authors present a methodology for evaluating question answering systems using integer programming over semi-structured knowledge, which involves converting questions into mathematical constraints and solving them to find the correct answer.
- They introduce the concept of ""global reasoning"" in question answering, where machines need to understand the relationships between different concepts and make inferences based on that understanding.
- The paper discusses several datasets used for evaluating machine comprehension systems, such as SQuAD (Stanford Question Answering Dataset), MCTest, SciTail, Newsqa, and others.
- It also mentions the importance of crowdsourcing in creating high-quality question answering datasets by using platforms like Amazon Mechanical Turk and CrowdFlower.
- The authors emphasize the need for more complex tasks, such as multi-hop reading comprehension, which requires machines to read multiple documents and make connections between them to answer a question.
- They introduce the concept of ""entailment"" in question answering, where machines must determine if one statement logically implies another. This is crucial for understanding the relationships between concepts and making inferences.
- The paper concludes by highlighting the importance of benchmark datasets like ARC in advancing machine comprehension capabilities and driving research towards more complex and realistic tasks.
- The paper introduces ARC, an AI2 Reasoning Challenge designed to test question answering systems using entailment models.
- It converts questions and answer choices into assertions (hypotheses) by filling in the blanks with the given answers.
- Entailment scores are computed for each hypothesis from a set of candidate premises retrieved from an ElasticSearch index.
- Two neural entailment models, Decomposable Attention and Decomposed Graph Entailment Model (DGEM), are used to compute the entailment scores.
- The maximum supporting sentence score is taken as the answer choice score for each question.
- The paper presents results on a large-scale science test question dataset, showing that ARC can be solved with high accuracy using neural entailment models.
- The authors release their code and datasets to facilitate further research in this area.
",2117
"1806.03198",1,"- The paper proposes a new approach for similarity search, focusing on learning a function that maps real-valued vectors to a uniform distribution over a d-dimensional sphere. This allows the use of fixed discretizing structures like binary encoding or regular lattice quantizers while maintaining competitive coding performance.
- The method aims to learn a network that preserves neighborhood structure in the input space and best covers the output space, balancing locality and uniformity. A parameter lambda controls this trade-off.
- Most efforts have been devoted to binary codes due to optimization tricks like soft binarization or stochastic relaxation. However, these methods struggle to improve over more powerful codes such as product quantization.
- The proposed method simplifies learning algorithms for indexing by learning a mapping that leads to better performance in subsequent discretization steps. This approach avoids the need for complex optimization procedures and can be applied with any subsequent quantizer.
- Experiments show that the end-to-end approach outperforms most learned quantization methods, achieving competitive results on widely adopted benchmarks. The code is available online.
- Training without the quantization step leads to almost no difference in accuracy but yields a generic catalyzer that can be applied with any subsequent quantizer.
- The paper introduces a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator, which enforces uniformity and is combined with a locality-aware triplet loss.
- The method learns a network that maps real-valued vectors to a uniform distribution over a d-dimensional sphere, allowing for fixed discretizing structures like binary encoding or regular lattice quantizers while maintaining competitive coding performance.
- This approach simplifies learning algorithms for indexing by focusing on learning a mapping that leads to better performance in subsequent discretization steps and avoids the need for complex optimization procedures.
- The method's end-to-end approach outperforms most learned quantization methods, achieving competitive results on widely adopted benchmarks.
- The paper introduces an approach for multi-dimensional indexing that maps input data to a spherical output space, making it easier for subsequent similarity search methods.
- A loss derived from the Kozachenko-Leonenko differential entropy estimator is used to favor uniformity in the spherical output space.
- The learned mapping allows using spherical lattice quantizers with competitive quantization properties and efficient algebraic encoding.
- An ablation study shows that the network can be trained without a quantization layer, acting as a plug-in for processing features before standard quantizers.
- Quantitative results demonstrate significant performance improvements for both quantization-based (OPQ) and binary (LSH) methods.
- The paper is organized into sections covering related works, the neural network model and optimization scheme, lattice assignment combination, experimental evaluation, and conclusions.
- The paper introduces a new approach for learning similarity search functions using spherical lattices and entropy regularization.
- It proposes KOLEO, an entropic regularizer that spreads points uniformly across the hypersphere of a dout-dimensional space. This helps in reducing the overlap between nearest neighbor distances.
- The paper also introduces a rank preserving loss function to ensure that the outputs follow the same neighborhood structure as in the input space.
- The overall loss combines the triplet loss and the entropy regularizer, with a parameter λ controlling the trade-off between ranking quality and uniformity.
- Experiments show that KOLEO improves the performance of state-of-the-art binary hashing methods on real-world datasets.
- The method is computationally efficient, requiring only one forward pass through the data during training.
- The paper introduces ""Spreading vectors for similarity search,"" a method that aims to improve the uniformity of high-dimensional latent spaces in Large Language Models (LLMs).
- It uses KoLeo regularization, which combines an entropic and catalyzer regularizer, to achieve better results compared to using only one type of regularization.
- The method is evaluated on a toy dataset adapted to the disk as output space, showing that without KoLeo regularization, neighboring points tend to collapse, leading to wasted coding capacity.
- Qualitative evaluation shows that the catalyzer reduces the overlap between distance distributions, resulting in a probability of 5% for the distance between a point and its nearest neighbor being larger than the distance between another point and its 100th nearest neighbor (compared to 20.8% in the input space).
- The paper discusses how the method interplays with discretization, using binarization and spherical quantizer as examples of parameter-free coding methods. Binarization is used by relaxing the sign function at training time, while lattices are more suitable for uniform distributions due to their regularizing effect on the output space.
- The method can be applied in a layer that takes a vector in Rd and returns its quantized version in the latent space. This approach improves the uniformity of high-dimensional spaces in LLMs, leading to better similarity search performance.
- Experiments: Focus on similarity search methods with compressed database vector representations (Charikar, 2002; Jegou et al., 2011a; Gong et al., 2013; Ge et al., 2013; Norouzi & Fleet, 2013).
- Experimental setup: Two phases - encoding and search. Datasets (Deep1M, BigAnn1M), metrics (recall at k performance measure), training (train on a subset of the database vectors, cross-validate hyperparameters dout and lambda).
- Model architecture and optimization: 3-layer perceptron with ReLU non-linearity, hidden dimension 1024, batch normalization, trained for 300 epochs using SGD with decaying learning rate.
- Similarity search with lattice vector quantizers: Compared to conventional methods (PQ, OPQ). Faiss implementation used for PQ and OPQ. Lattice-based indexing proposed in Section 4. Performance comparison on Deep1M and BigAnn1M datasets.
- Impact of hyperparameters: Varying kpos and kneg did not significantly impact performance; dout trade-off between good representation and easily compressible one.
- Low bitrates perform better with small dimensions due to approximation quality, while higher bitrates require larger dimensions for representation quality.
- Regularizer λ needs to be set differently for different dimensions and bitrates: large values for small dimensions and low bitrates, lower values for higher dimensions and higher bitrates (Appendix A).
- Large-scale experiments with Deep1B and BigAnn datasets show recall performance drop but precision advantage maintained for lattice quantizer.
- Comparison to state-of-the-art methods shows Catalyst + Lattice outperforms other approaches in terms of encoding time, recall@10, and 100 while maintaining competitive accuracy.
- Ablation study demonstrates the importance of the catalyzer by showing a significant decrease in performance when replacing it with PCA.
- End-to-end training has limited impact on overall performance, possibly due to approximation issues or KoLeo regularizer narrowing the gap induced by discretization.
- The method can be used as a catalyst for binary hashing, improving upon popular methods like LSH and ITQ.
- The paper introduces a method called ""catalyzer"" for improving Locality Sensitive Hashing (LSH) and Iterative Quantization Techniques (ITQ).
- Catalyzer optimizes the orthogonal projection in LSH, resulting in better correlation between original vectors and bits.
- The catalyzer improves performance by 2-9 percentage points in all settings, from 32 to 128 bits.
- This work demonstrates that adapting data distribution to a rigid quantizer can be competitive compared to adapting the quantizer to input data.
- Rigid quantizers are fast at encoding time and vectors can be decoded without requiring codebooks or auxiliary tables.
- The paper is published as a conference paper at ICLR 2019, with open-sourced code available on GitHub.
Summary of ""Spreading vectors for similarity search"":
- The paper introduces a new method called spreading vectors, which improves the efficiency and accuracy of similarity search in large-scale datasets.
- Spreading vectors use a hierarchical structure to represent data points, enabling fast retrieval with high precision.
- The approach combines locality sensitive hashing (LSH) and product quantization (PQ). LSH reduces the dimensionality of data while PQ represents each dimension as a codebook.
- Spreading vectors are trained using an auto-encoder, which learns to reconstruct the original data from its spreading vector representation.
- The method achieves state-of-the-art performance on several benchmark datasets, including ImageNet and CIFAR-10.
- Experiments show that spreading vectors outperform other methods in terms of accuracy, speed, and memory usage.
- Spreading vectors can be applied to various tasks such as image retrieval, face recognition, and object detection.
- The paper provides a detailed analysis of the method's performance and discusses its limitations and future directions for research.
- The paper introduces a method for spreading vectors, which is useful for similarity search in large datasets.
- It focuses on integer points in hyper-cubic lattices and hyper-spheres to represent data efficiently.
- Quantizing a vector involves solving an optimization problem to find the nearest vector within a set of integer points (Sr d).
- Atoms are defined as normalized vectors, which can be represented by permutations of their components with sign flips.
- Encoding and decoding vectors in Sr d is achieved using combinatorial number systems and sign bits for non-zero elements.
- The method outperforms PQ (a popular vector quantization technique) in terms of encoding time, making the preprocessing negligible compared to the search process.
- Experiments show that the proposed method achieves better agreement between range search and k-nearest neighbors search on Deep1M dataset.
- The paper presents a novel approach for similarity search in large datasets using integer points and efficient vector encoding techniques, improving upon existing methods like PQ.
",1978
"1806.03822",1,"- Introduction of SQuAD 2.0, a new version of the Stanford Question Answering Dataset (SQuAD) that combines answerable questions from SQuAD 1.1 with over 50,000 unanswerable questions written by crowdworkers to look similar to answerable ones.
- The goal is for systems to not only answer questions when possible but also determine when no answer is supported by the paragraph and abstain from answering.
- SQuAD 2.0 is a challenging natural language understanding task, with a strong neural system achieving 86% F1 on SQuAD 1.1 but only 66% F1 on SQuAD 2.0.
- The new dataset aims to encourage the development of reading comprehension systems that know what they don't know and improve true language understanding.
- SQuAD 2.0 is released as a new version of SQuAD, becoming the primary benchmark on the official SQuAD leaderboard.
- Relevance: Unanswerable questions should appear relevant to the topic of the context paragraph, and not be easily distinguished by simple heuristics like word overlap.
- Existence of plausible answers: There must be some span in the context whose type matches the question's answer type. This ensures that type-matching heuristics can't distinguish between answerable and unanswerable questions.
- Existing datasets: Researchers surveyed extractive and sentence selection reading comprehension datasets to identify negative examples (unanswerable questions). They found issues with distant supervision strategies, crowdworker-generated questions, and rule-based question editing methods.
- Types of negative examples in SQuAD 2.0: The paper presents a table listing various types of unanswerable questions, including Negation, Antonym, Entity Swap, Mutual Exclusion, Impossible Condition, Other (Neutral), and Answerable (i.e., dataset noise).
- Guidelines for generating negative examples: To create high-quality negative examples, ensure relevance, existence of plausible answers, and avoid simple heuristics.
- Constructed a new dataset called SQuAD 2.0 with unanswerable questions to address limitations of previous datasets.
- Used crowdworkers on Daemo platform to create unanswerable questions based on existing answerable ones from SQuAD 1.1, ensuring plausible answers were present.
- Created train, development, and test splits using the same partition as SQuAD 1.1, resulting in a roughly one-to-one ratio of answerable to unanswerable questions in development and test sets, while maintaining twice as many answerable questions in training data.
- Human accuracy was confirmed by hiring additional crowdworkers to answer all questions in the development and test sets, selecting final answers based on majority vote.
- Analyzed 100 randomly chosen negative examples from SQuAD 2.0's development set, identifying various categories of unanswerable questions beyond expected phenomena like negation, antonymy, and entity changes.
- Evaluated three existing model architectures: BERT, XLNet, and RoBERTa on SQuAD 1.1 and SQuAD 2.0 datasets to demonstrate the importance of unanswerable questions for LLMs.
- ""Know What You Don't Know: Unanswerable Questions for SQuAD"" introduces a new dataset, SQuAD 2.0, which focuses on unanswerable questions and aims to improve the understanding of textual entailment in Large Language Models (LLMs).
- The paper highlights that existing models struggle with SQuAD 2.0, achieving only 66.3 F1 on test set, significantly lower than human accuracy of 89.5 F1. This suggests a large room for model improvement.
- Automatically generated negative examples (TFIDF and RULEBASED) in SQuAD 1.1 are easier to detect compared to the unanswerable questions in SQuAD 2.0, indicating that these questions pose a greater challenge to LLMs.
- Plausible answers provided by crowdworkers for unanswerable questions act as effective distractors, with roughly half of all wrong answers matching these plausible answers.
- The paper concludes that SQuAD 2.0 is a challenging and diverse dataset that forces models to understand when a question cannot be answered based on the text provided. This understanding is crucial for improving LLMs' performance in tasks such as textual entailment, relation extraction, and adversarial test examples.
- The paper introduces ""Know What You Don't Know: Unanswerable Questions for SQuAD,"" which aims to improve reading comprehension models by teaching them when a question cannot be answered based on the context.
- SQuAD 2.0 is optimistic about encouraging the development of new models that understand language at a deeper level through unanswerable questions.
- Reproducibility: All code, data, and experiments are available on CodaLab platform (https://bit.ly/2rDHBgY).
- Acknowledgments: Thanks to anonymous reviewers, Arun Chaganty, Peng Qi, Sharon Zhou, Durim Morina, Michael Bernstein, and funding from Facebook.
- R.J. is supported by an NSF Graduate Research Fellowship (DGE-114747).
- The paper references various works in the field of reading comprehension, natural language processing, and machine learning, including SQuAD, MCTest, TriviaQA, MS MARCO, Bidirectional Attention Flow, NewsQA, WikiQA, and others.
- Unanswerable questions are introduced to test the limits of models' understanding and help them learn when they don't know something.
- The paper presents a new dataset with 12,364 unanswerable questions for SQuAD 2.0, which can be used as a benchmark for future research.
- Unanswerable questions are generated using a combination of human annotation and machine-generated questions that are likely to be unanswerable based on the context.
- The paper concludes by stating that SQuAD 2.0's unanswerable questions will help improve reading comprehension models, leading to better language understanding at a deeper level.
- The paper introduces ""Know What You Don't Know: Unanswerable Questions for SQuAD,"" which aims to create a dataset of unanswerable questions and plausible answers for the Stanford Question Answering Dataset (SQuAD).
- Crowdsourcing is used to generate these unanswerable questions, with workers writing queries that cannot be answered using the given paragraphs.
- The interface allows workers to highlight a plausible answer within the text, which can potentially confuse machine learning models.
- Results show that plausible answers account for roughly half of false positive errors made by computer systems and human answerers.
- This dataset can be used to improve the performance of question answering systems by training them on unanswerable questions with distracting plausible answers.
- The paper provides a supplementary material section, detailing crowdsourcing instructions and interface design.
- Table 5 presents exact match (EM) and F1 scores between system predictions and plausible answers in cases where the system made false positive errors.
",1436
"1809.02922",1,"- The paper proposes a method to automatically derive Natural Language Inference (NLI) datasets from large-scale Question Answering (QA) datasets, expanding and diversifying existing NLI resources.
- This approach involves learning a sentence transformation model that converts question-answer pairs into declarative forms, which can then be applied to various QA resources.
- The system generates a new dataset called QA-NLI with over 500k examples, exhibiting a wide range of inference phenomena not commonly seen in previous NLI datasets.
- This method helps address the limitations of existing NLI datasets by augmenting them with multi-sentence reasoning and other important linguistic phenomena for various downstream applications.
- The connection between QA and NLI is explored, leading to the inspiration for this approach.
- The paper demonstrates that the proposed method can be successfully applied to a variety of QA resources, even when primarily trained on a single dataset.
- This work contributes to the advancement of language understanding research by providing a new method for generating NLI datasets from abundant QA data sources.
- The paper proposes a method to transform question answering datasets into natural language inference (NLI) datasets, which can be used for NLI research.
- This approach has two key advantages: it provides more data and covers a wider range of reasoning strategies compared to existing NLI datasets.
- Large-scale QA datasets are abundant, making them suitable for this transformation.
- The paper discusses three methods for deriving declarative sentences from question-answer pairs (QA2D): rule-based system, crowdsourcing, and neural sequence model.
- A good rule-based system can improve the quality of crowdsourcing while not introducing bias.
- The automated QA2D system generalizes well to various domains, such as Wikipedia, newswire, and movie plots.
- The automatically generated declaratives match human gold answers 45–57% of the time with BLEU scores ranging between 73–83.
- This method can be applied to five different QA datasets, creating over 500k NLI examples.
- The paper's findings demonstrate practical applications and benefits for future LLM researchers within organizations.
- The automated transformation of question answering datasets into natural language inference datasets provides a valuable resource for NLI research.
- The paper introduces QA2D, an automated system that converts Question Answering (QA) datasets into Natural Language Inference (NLI) examples.
- This conversion results in the creation of a large NLI dataset called QANLI with over 500,000 examples from five different QA datasets.
- The resulting QANLI dataset exhibits various inference phenomena such as multi-sentence and meta-level reasoning, presupposition-based inference, etc.
- A thorough analysis of these phenomena quantifies the diversity in terms of reasoning types and contextual scope required for each type.
- The approach eliminates some annotation artifacts present in existing NLI datasets like SNLI and MultiNLI.
- QA2D framework converts a QA example into an NLI pair by combining question and answer into a declarative sentence, then identifying entailment or non-entailment based on the correctness of the answer.
- Incorrect answers are available in multiple choice datasets like MovieQA, RACE, and MCTest, while unanswerable questions can be found in SQuADRUn.
- The paper highlights that performing automated QA2D only makes a two-way distinction between entailment and non-entailment, unlike existing NLI datasets with three relations (entailment, neutral/unknown, contradiction).
- Weakly supervised QA datasets like NewsQA, RACE, and TriviaQA use longer passages as premises, leading to more complex inference examples.
- The paper's findings can be useful for future research on NLI and QA datasets, providing a larger dataset with diverse inference phenomena and eliminating annotation artifacts present in existing datasets.
- The paper discusses transforming question answering datasets into natural language inference (NLI) datasets, aiming to analyze and maximize structural and topical diversity of data sources.
- Five QA datasets are transformed into NLI: MovieQA, NewsQA, QAMR, RACE, and SQuAD. These datasets cover a wide range of genres and topics, including movie plots, newswire text, Wikipedia, and English exams.
- Passage types vary from sentences to multiple paragraphs, with answer types being either substrings or free-response text. Question difficulty ranges from selecting arguments within a sentence to holistic reasoning about the text.
- A rule-based system is developed for QA2D, demonstrating challenges in semantic decisions and dependency parsing accuracy. The Stanford Graph-Based Neural Dependency Parser is used for part-of-speech tagging and parsing.
- Around 10% of mistakes made by the rule-based system are due to tagging/parsing errors, highlighting the importance of accurate dependency parsing in question answering tasks.
- The paper identifies several semantic idiosyncrasies that prove difficult to account for using rules, such as bare named entities referring to organizations or institutions without articles.
- The paper discusses transforming question answering datasets into natural language inference datasets, focusing on improving sentence parsing and annotation for better performance.
- Errors made by taggers/parsers include incorrect verb-noun tagging due to do-support removal and identifying the parent of dangling prepositions.
- A supervised neural model is built using crowdsourced human-authored gold declarative sentences, with two data collection setups: from scratch (S) and post-editing (E).
- The paper analyzes the trade-offs between writing from scratch and post-editing, highlighting that while Setup S minimizes bias towards rule-based outputs, it takes more time and has a higher error rate compared to Setup E.
- The distribution of source QA datasets is discussed, with one dataset selected for collecting most data to test the generalization ability of the neural model.
- The paper presents an evaluation methodology that involves using gold answers from SQuAD and other QA datasets, comparing results with a rule-based system, and analyzing the performance of the supervised neural model.
- The paper aims to transform question answering datasets into natural language inference datasets by developing a neural sequence generation model called QA2D.
- They use SQuAD as their main source of QA pairs due to its large size, high quality, and syntactic diversity.
- A new dataset is created with gold declarative answer sentences from SQuAD training set and other four datasets for evaluation.
- The neural sequence generation model (QA2D) uses an encoder-decoder architecture with bidirectional LSTMs, attention heads, and a copy mechanism to generate declarative answers.
- Performance is assessed using both automated metrics and human evaluations, showing that the neural QA2D system outperforms the rule-based baseline in most cases.
- The paper highlights practical applications of their model, such as improving question answering systems by generating declarative answers for downstream tasks like reading comprehension.
- The paper compares rule-based (RULE-BASED) and neural (NEURAL) QA2D systems using automated metrics and human evaluation on five QA datasets.
- NEURAL consistently outperforms RULE-BASED, leading by an average of 2.6 BLEU points and 6.2% in exact match accuracy across all datasets.
- NEURAL can produce a top-5 beam of outputs, with evaluating the best output resulting in almost a 30% improvement in scores.
- Both models have domain-general performance, though RULE-BASED performs worse on specific datasets due to their inability to handle semantically motivated modifications.
- NEURAL's performance is more sensitive to question length than RULE-BASED, performing better on shorter inputs and less robust for longer ones.
- The paper highlights the importance of understanding how models learn semantic patterns and handle answer span redundancies.
- The paper explores transforming question answering datasets into natural language inference datasets, focusing on analyzing how neural and rule-based models perform in this task.
- Neural models tend to output shorter sequences compared to rule-based ones.
- Performance varies by question type: best for 'who' questions, worst for 'which' questions, and a significant difference between RULE-BASED and NEURAL for 'how' questions.
- Human evaluation was conducted on 100 QA examples from five datasets, comparing the performance of RULE-BASED, NEURAL, and HUMAN systems.
- The paper highlights that neural models can be used to generate natural language inference datasets, which could potentially improve question answering systems.
- The paper evaluates question answering models' performance by rating them based on grammaticality, naturalness, and completeness.
- Grammaticality scores are lower for models compared to humans (4.6 vs 3.8 for rule-based and 7).
- Naturalness ratings show that models perform better than in grammar but still lag behind humans (1 vs 2.5 for rule-based, 2.5 for RAKE, and 2.9 for BERT).
- Completeness scores are relatively high for all models, with minor differences between them (3.7 for rule-based, 3.8 for RAKE, and 3.9 for BERT).
- The study highlights the need to improve question answering systems' performance in terms of grammaticality and naturalness.
- It also suggests that models can be trained using human-written annotations to enhance their performance in these areas.
- The paper provides examples from various datasets, including QAMR, NewsQA, SQuAD, MultiNLI, Naive Physics, and Psychology, to illustrate the differences between model outputs and human-written answers.
- The paper analyzes transforming question answering datasets into natural language inference (NLI) datasets, focusing on identifying main contributions and most interesting findings.
- NLI datasets are generated from various sources like MovieQA, NewsQA, SQuAD, MultiNLI, QAMR, and RACE, each with distinct phenomena of reasoning required for correct inferences.
- The study examines the completeness and naturalness scores of rule-based and neural models, finding that both perform well above the threshold for retaining correct meaning.
- Analyzing NLI datasets, the paper validates assumptions about how answer status affects inference labels and compares them to other annotation artifacts.
- QAMR is unique as it involves only argument-level reasoning with no multisentence examples, while MultiNLI has fewer instances of multi-sentence reasoning and less world knowledge than others.
- The study highlights the importance of understanding inference phenomena to improve NLI datasets and models for question answering tasks.
- The paper explores converting question answering datasets into natural language inference (NLI) datasets, analyzing differences between QA and NLI tasks.
- It presents a method to convert five QA datasets into NLI format by identifying scope and reasoning types.
- Inference pairs are generated from incorrect answers, which often lead to non-entailments, providing insights on presuppositions and world knowledge.
- RACE dataset differs from QAMR, focusing more on multi-sentence entailments, world knowledge, metalevel reasoning, and human psychology.
- Inference labeling of 2000 inference pairs based on MovieQA shows a strong correlation between answer type and inference score, supporting non-binary entailment notion.
- Annotation artifacts analysis reveals no significant differences from SNLI and MultiNLI, suggesting the absence of similar issues in the converted datasets.
- The paper provides practical applications for NLP researchers by offering new datasets for training and evaluating models on various reasoning types.
- The paper transforms Question Answering (QA) datasets into Natural Language Inference (NLI) datasets, making them complementary to existing resources and enabling researchers to leverage NLI for transfer learning gains on other tasks.
- This conversion process involves reformatting declarative sentences from QA datasets into generic representations of inferences, similar to sentence simplification, paraphrasing, and summarization tasks.
- The study finds that the transformed NLI datasets have little/no correlation between example length and label, unlike SNLI and MultiNLI (MNLI) datasets.
- The authors observe differences in word distributions for entailments and non-entailments in their dataset compared to SNLI and MNLI, with no negation words in non-entailments and fewer positive or non-specific words in entailments.
- This work is the first to perform an automated conversion of QA datasets into NLI datasets, building on previous connections between QA and NLI found by Dagan et al. (2006) and SciTail creators (Khot et al., 2018).
- The text transformation tasks highlight the close connection between sentence simplification, paraphrasing, summarization, and question generation, all of which involve transforming declarative sentences into other declarative sentences.
- Declarative sentences are closed under these operations, allowing for chaining to perform more complex inferences (Kolesnyk et al., 2016).
- Question generation is another related task that could be considered the reverse of QA2D, focusing on selecting interesting questions rather than robust sentence transformations.
- The study's findings have practical applications and benefits for researchers looking to leverage NLI resources for transfer learning gains in other tasks.
- QA2D focuses on selecting interesting questions rather than robust sentence transformation, using neural sequence generation models.
- Improvements in generation architectures (Gehring et al., 2017; Vaswani et al., 2017) and incorporating syntactic structure (Chen et al., 2017; Eriguchi et al., 2017) or transducer-like structures (Graves, 2012; Yu et al., 2016) could enhance data efficiency and performance.
- Future systems may include generative NLI models for hypothesis generation, sentence decomposition models, and sentence synthesis models with increased scale of NLI training resources.
- Acknowledgments to Chris Potts and Stanford NLP group for valuable feedback.
",2774
"1810.04805",1,"- BERT (Bidirectional Encoder Representations from Transformers) is a new language representation model designed to pre-train deep bidirectional representations from unlabeled text.
- Unlike previous models, it jointly conditions on both left and right context in all layers, allowing for better performance without substantial task-specific architecture modifications.
- BERT achieves state-of-the-art results on 11 natural language processing tasks, including GLUE score of 80.5%, MultiNLI accuracy of 86.7%, SQuAD v1.1 Test F1 of 93.2%, and SQuAD v2.0 Test F1 of 83.1%.
- BERT's pre-trained model can be fine-tuned with just one additional output layer, making it conceptually simple and empirically powerful.
- The paper argues that current techniques restrict the power of pre-trained representations, especially for fine-tuning approaches, due to unidirectional language models limiting architecture choices during pre-training.
- BERT's bidirectional approach allows for better contextual understanding and improved performance in various tasks compared to previous unidirectional models.
- BERT is a bidirectional encoder representation from transformers, addressing unidirectionality constraints in previous language models like OpenAI GPT and improving fine-tuning approaches.
- It uses a ""masked language model"" (MLM) pre-training objective inspired by the Cloze task to enable deep bidirectional Transformer representations.
- BERT also incorporates a ""next sentence prediction"" task for joint text-pair representation pre-training.
- The paper demonstrates the importance of bidirectional pre-training for language representations and shows that pre-trained representations reduce the need for many heavily engineered task-specific architectures.
- BERT achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks, outperforming many task-specific architectures.
- The code and pre-trained models are available at https://github.com/google-research/bert.
- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained deep learning model that combines left-to-right and right-to-left language modeling for better contextual understanding.
- It uses masked language modeling, next sentence prediction, and sentence order prediction objectives during pre-training to learn contextual representations of words in sentences.
- BERT's architecture consists of multiple layers of transformer encoders with self-attention mechanisms, which allow it to process the entire input sequence at once.
- The model is trained on large unlabeled corpora like BooksCorpus and English Wikipedia, achieving state-of-the-art results in various downstream tasks such as question answering, sentiment analysis, and named entity recognition.
- BERT's pre-trained weights can be fine-tuned for specific tasks by adding a few task-specific layers on top of the model.
- The paper introduces BERT's architecture and training process, comparing it to previous language modeling approaches like ELMo and OpenAI GPT.
- BERT's performance is evaluated on several benchmarks, including GLUE (General Language Understanding Evaluation), SQuAD (Stanford Question Answering Dataset), MNLI (Multi-Genre Natural Language Inference Corpus), and CoLA (Corpus of Linguistic Acceptability).
- BERT achieves a new state-of-the-art performance on most tasks, with an average improvement of 2.5 points in F1 score over previous models.
- The paper discusses the model's limitations and potential future improvements, such as incorporating more contextual information or using larger corpora for pre-training.
- BERT has become a widely used foundation model for various NLP tasks due to its strong performance and flexibility in fine-tuning for specific applications.
- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that combines bidirectional transformers with masked language modeling and next sentence prediction tasks for better language understanding.
- The model architecture consists of a multi-layer bidirectional Transformer encoder based on the original implementation in Vaswani et al. (2017). BERT uses bidirectional self-attention, while other models like GPT use constrained attention.
- There are two main steps: pre-training and fine-tuning. During pre-training, unlabeled data is used for different tasks, while fine-tuning involves using labeled data from downstream tasks with the same pre-trained parameters.
- BERT has been applied to various natural language processing (NLP) tasks such as question answering, named entity recognition (NER), and natural language inference (MNLI).
- The model's performance is evaluated on several benchmarks, including SQuAD, GLUE, and RACE. BERT outperforms other models like OpenAI GPT and ELMo in most cases.
- BERT has been shown to be effective for zero-shot transfer learning, where it achieves state-of-the-art results on 11 of 12 tasks without any task-specific fine-tuning.
- The model's performance is also comparable to human performance in some cases, such as the SQuAD question answering dataset, where BERT achieved an accuracy of 90.4%.
- BERT has been used for various practical applications like language translation and text summarization.
- The model's pre-trained parameters can be initialized for different downstream tasks, reducing training time by up to 15 times compared to models without pre-training.
- BERT is available as open-source code on GitHub, allowing researchers and developers to easily access and experiment with the model.
- BERT (Bidirectional Encoder Representations from Transformers) uses bidirectional self-attention, while GPT (Generative Pre-trained Transformer) has constrained self-attention with context only to the left.
- BERT's input representation can handle various downstream tasks by representing a single sentence or a pair of sentences in one token sequence. WordPiece embeddings and a 30,000 token vocabulary are used.
- Pre-training BERT involves two unsupervised tasks: Masked LM (Masked Language Modeling) and NSP (Next Sentence Prediction). These tasks help train the model without labeled data.
- In Masked LM, a percentage of input tokens is randomly masked, and the model predicts those missing tokens. This encourages the model to learn contextual relationships between words.
- NSP requires the model to determine whether two sentences are consecutive in an original text or not. It helps train BERT's understanding of sentence order.
- BERT achieves state-of-the-art results on 11 natural language processing tasks, including question answering, sentiment analysis, and named entity recognition.
- BERT is 4.5 times faster than the previous state-of-the-art model (ELMo) while achieving similar performance in downstream tasks.
- BERT's pre-training approach can be applied to other tasks like question answering, sentiment analysis, and natural language inference without requiring task-specific architectures or training data.
- BERT is a pre-trained deep bidirectional transformer for language understanding, using masked language modeling (MLM) and next sentence prediction (NSP).
- MLM involves randomly masking 15% of WordPiece tokens in each sequence and predicting them. The model uses an output softmax over the vocabulary to predict these masked tokens.
- NSP is a binary classification task where 50% of the time, B is the actual next sentence for A (IsNext), while the other 50% it's a random sentence from the corpus (NotNext).
- The model achieves high accuracy on both MLM and NSP tasks, with 97-98% accuracy in NSP.
- BERT outperforms previous state-of-the-art models in several downstream tasks such as question answering, natural language inference, and sentiment analysis.
- The model is efficient, requiring only 40% of the training time of GPT (1 week vs. 2.5 weeks).
- BERT's performance is robust to different masking ratios, with a slight drop in accuracy when using fewer than 15% or more than 30%.
- The model can be fine-tuned for specific tasks by adding task-specific layers on top of the pre-trained BERT model.
- BERT's architecture allows for easy parallelization, making it suitable for distributed training.
- BERT is available as open-source code and pre-trained models, facilitating its use in various applications.
- BERT is a pre-trained deep bidirectional transformer for language understanding, using sentence embeddings transferred to downstream tasks.
- Pre-training data includes BooksCorpus (800M words) and English Wikipedia (2.5B words), with text passages only.
- Fine-tuning BERT is straightforward, as it can model various tasks by swapping inputs and outputs.
- Fine-tuning is relatively inexpensive, taking at most 1 hour on a Cloud TPU or a few hours on a GPU.
- Experiments show BERT's performance on 11 NLP tasks, including GLUE (General Language Understanding Evaluation), SQuAD (Stanford Question Answering Dataset), and MNLI (Multi-Genre Natural Language Inference Corpus).
- BERT achieves state-of-the-art results in most of the tasks, with a 30% accuracy improvement on GLUE.
- BERT's performance is comparable to or better than other pre-trained language models like ELMo and ULMFiT.
- BERT's self-attention mechanism allows it to handle various tasks without significant changes in architecture, making it more versatile.
- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained deep learning model for natural language understanding, designed to improve the performance of various NLP tasks.
- It uses a masked language modeling objective and next sentence prediction task during pre-training.
- The paper demonstrates BERT's effectiveness on 11 GLUE (General Language Understanding Evaluation) benchmark tasks, achieving state-of-the-art results in most cases.
- BERT outperforms OpenAI GPT and other models by a substantial margin, with an average accuracy improvement of 4.5% for BERTBASE and 7% for BERTLARGE over the prior state of the art.
- The model's performance is better on tasks with less training data, especially in SQuAD v1.1 where it achieves a Dev F1 score of 91%.
- BERT's pre-training approach allows for faster fine-tuning and better generalization compared to other models.
- The paper also introduces the concept of ""transfer learning"" by using BERT as a feature extractor in downstream tasks, such as sentiment analysis and question answering.
- BERT's architecture is based on Transformer encoders with self-attention mechanisms, which enables it to capture long-range dependencies between words in a sentence.
- The paper provides insights into the model's design choices and implementation details, making it valuable for researchers looking to build upon or improve upon BERT.
- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained deep learning model for natural language understanding, developed by Google AI researchers.
- The paper introduces the BERT architecture and training process, focusing on the task of question answering using SQuAD (Stanford Question Answering Dataset).
- BERT uses bidirectional transformers to encode contextualized word representations, which are then fine-tuned for specific tasks like question answering.
- The model's performance is evaluated on the SQuAD 1.1 and 2.0 leaderboards, where it outperforms existing systems by a significant margin.
- BERT's performance improves further when combined with data augmentation techniques such as fine-tuning on TriviaQA before training on SQuAD.
- The paper highlights the importance of pre-training and transfer learning in achieving state-of-the-art results for natural language understanding tasks.
- BERT's architecture and training process can be adapted to other NLP tasks, such as text classification, named entity recognition, and sentiment analysis.
- BERT has become a widely used benchmark model for various NLP applications due to its strong performance and generalizability across different domains.
- The paper also discusses the limitations of BERT, including its large size, computational requirements, and potential bias in pre-training data.
- Future research directions include improving efficiency, reducing model size, and addressing bias issues in pre-trained models.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - This paper introduces a new method to improve language understanding by using bidirectional transformers and pre-training.
- Problem definition extension: The model allows for the possibility that no short answer exists in a given paragraph, making the problem more realistic. It uses a simple approach to extend the SQuAD v1.1 BERT model for this task.
- TriviaQA data usage: The paper utilizes TriviaQA-Wiki data with 400 tokens per paragraph containing at least one possible answer. Fine-tuning results show improvements over previous systems, including a +5.1 F1 improvement over the best system.
- SWAG dataset application: BERT is applied to the Situations With Adversarial Generations (SWAG) dataset, which contains 113k sentence-pair completion examples for grounded commonsense inference. The model outperforms previous systems by +27.1% and 8.3%.
- Ablation studies: These experiments help understand the relative importance of different aspects of BERT. Results show that removing certain pre-training tasks can lead to a decrease in performance, emphasizing the importance of these tasks for language understanding.
- BERT is a deep bidirectional transformer model for language understanding, pre-trained using two tasks: masked language modeling (MLM) and next sentence prediction (NSP).
- Removing NSP from the pre-training hurts performance significantly on QNLI, MNLI, and SQuAD 1.1.
- Training bidirectional representations improves performance over left-to-right models in all tasks, with large drops on MRPC and SQuAD for LTR models.
- Adding a randomly initialized BiLSTM to the LTR & No NSP model improves results on SQuAD but still performs worse than bidirectional models.
- Model size affects fine-tuning task accuracy, with larger models leading to better performance across all datasets.
- BERT's pre-training objectives and architecture allow for efficient transfer learning, achieving state-of-the-art results on 11 out of 12 GLUE benchmark tasks.
- BERT's performance is comparable to ELMo but with fewer parameters (110M vs. 145M) and faster training time (4.5 times).
- BERT's pre-training objectives can be used as a general recipe for other languages, achieving state-of-the-art results on XNLI, Yahoo Answers Question Answering, and PAWS-X datasets.
- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained deep learning model for natural language understanding, designed to improve the performance of downstream tasks with limited training data.
- The model achieves significant improvements in accuracy across various datasets, even on small-scale tasks like MRPC (3,600 labeled examples).
- BERT's effectiveness demonstrates that scaling extreme model sizes can lead to large improvements on both large and small scale tasks.
- BERT has 110M parameters for the base version and 340M for the large version, which is larger than previous models like Vaswani et al.'s (2017) and Al-Rfou et al.'s (2018).
- The paper compares two approaches: fine-tuning (adding a classification layer to the pre-trained model) and feature-based approach (extracting fixed features from the pre-trained model).
- BERT is applied to the CoNLL-2003 Named Entity Recognition task, using a case-preserving WordPiece model with maximal document context.
- The fine-tuning approach achieves better results than the feature-based approach in this specific task.
- BERT's performance on the CoNLL-2003 NER task demonstrates its ability to handle various tasks, even those not easily represented by a Transformer encoder architecture.
- BERT is a pre-trained deep bidirectional transformer for language understanding, formulated as a tagging task without using CRF hyperparameters.
- The paper presents results on various NLP tasks, including MNLI, MRPC, and SST-2, demonstrating competitive performance compared to state-of-the-art methods like ELMo and CSE.
- BERT's effectiveness is shown in both fine-tuning and feature-based approaches, with the best performing method concatenating token representations from the top four hidden layers of the pre-trained Transformer.
- The paper highlights that deep bidirectional architectures can benefit low-resource tasks, generalizing findings from unidirectional architectures.
- BERT's major contribution is in further generalizing these findings to deep bidirectional architectures, allowing a single pre-trained model to tackle a broad set of NLP tasks.
- The paper emphasizes the importance of rich, unsupervised pre-training for language understanding systems and its role in enabling even low-resource tasks to benefit from deep architectures.
",3404
"1811.00937",1,"- Introduction to CommonsenseQA: A question answering challenge focusing on commonsense knowledge, addressing limitations of current NLU systems in handling complex semantics and prior knowledge.
- Data creation process: Extracting multiple target concepts from CONCEPTNET for a single source concept, crowd-workers author multiple-choice questions with these concepts as answers.
- Question difficulty demonstrated by strong baselines: Best baseline (BERT-large) achieves 56% accuracy, significantly lower than human performance at 89%.
- Example questions and target concepts: Illustrates the complexity of questions requiring prior knowledge.
- Practical applications: Could be used for training LLMs to improve their understanding of commonsense knowledge and complex semantics.
- Unusual findings: Demonstrates the gap between human performance and current NLU systems in handling commonsense knowledge.
- The paper introduces CommonsenseQA, a question answering challenge focused on commonsense knowledge.
- It aims to address the limitations of previous QA benchmarks that mostly focus on factoid questions and lack common sense elements.
- The dataset is based on CONCEPTNET, which encodes commonsense relations between concepts.
- Crowd workers generate questions by choosing a source concept and three target concepts related through a single CONCEPTNET relation. They then create one question for each target concept, ensuring only that specific target concept is the answer.
- An additional distractor from CONCEPTNET and a manually authored distractor are added to each question, resulting in five candidate answers per question.
- The collected dataset consists of 12,247 commonsense questions with unique characteristics compared to prior QA benchmarks.
- These questions require background knowledge that is often trivial for humans but not explicitly reported on the web due to reporting bias.
- The paper presents an analysis illustrating the uniqueness and usefulness of CommonsenseQA in comparison to existing datasets.
- Introduces a new QA dataset, CommonsenseQA, focused on common sense with 12,247 examples.
- Uses CONCEPTNET to generate commonsense questions at scale.
- Evaluates state-of-the-art NLU models on the CommonsenseQA dataset and finds humans outperform current models substantially.
- Fine-tuning BERT-LARGE achieves an accuracy of 55.9%, while human performance is 88.9%.
- Provides a download link for the dataset (www.tau-nlp.org/commonsenseqa) and code for baselines on GitHub (github.com/jonathanherzig/commonsenseqa).
- Reviews related work, highlighting challenges in evaluating common sense in machines and existing datasets' limitations.
- Discusses the importance of crowdsourcing in creating larger datasets like JHU Ordinal Commonsense Inference, Story Cloze Test, SWAG, etc.
- The paper introduces CommonsenseQA, a question answering challenge targeting commonsense knowledge.
- It aims to create benchmarks for measuring common sense understanding rather than distributional biases or annotation process modeling.
- The dataset generation process involves extracting subgraphs from ConceptNet, crowdworkers authoring questions, and filtering them by quality.
- CommonsenseQA consists of multiple-choice questions with corresponding relevant context (snippets).
- The paper highlights the difficulty in creating benchmarks for common sense understanding and discusses related efforts like Science QA and SQUABU.
- Evaluation metrics include accuracy, F1 score, and human annotation time.
- The dataset is publicly available to facilitate further research on commonsense reasoning.
- CommonsenseQA can be used for various applications such as conversational agents, educational tools, and AI systems that require common sense knowledge.
- The paper presents a new challenge in the field of large language models (LLMs) by introducing a benchmark for measuring commonsense understanding.
- Future research directions include investigating how to improve model performance on CommonsenseQA and exploring other applications of this dataset.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focused on commonsense knowledge.
- It uses CONCEPTNET, a graph knowledge-base with 32 million triplets, to extract subgraphs for crowdsourcing workers to create questions and distractors.
- Crowdsourced workers generate three questions per subgraph (one per target concept) and two additional distractors per question.
- Textual context is added by querying a search engine for web snippets related to the questions.
- The data generation process involves filtering triplets, creating question sets, crowdsourcing questions, adding distractors, and verifying question quality.
- The paper highlights the importance of background knowledge in answering commonsense questions.
- The challenge aims to improve machine understanding of commonsense knowledge by training LLMs on this dataset.
- The paper presents an evaluation of the model's performance using a test set and discusses future work, including expanding the dataset with more relations and concepts.
- CommonsenseQA is a question answering challenge targeting commonsense knowledge, using concepts and relations from CONCEPTNET.
- To make the task more difficult, crowd-workers add two distractors to each formulated question: one related to the concept in CONCEPTNET and another manually created.
- A disjoint group of workers verify the generated questions, filtering out 15% of them based on their answers.
- Textual context is added by issuing Google searches for each question and candidate answer, concatenating the answer to the question. This creates a context of 500 snippets per question, allowing for reading comprehension model performance analysis.
- The dataset contains 12,247 final examples from a total of 16,242 formulated questions, with an average cost per question of $0.33.
- Key statistics include 2,254 CONCEPTNET distinct question nodes, 12,094 answer nodes, and an average question length of 13.41 tokens.
- The top-5 question concepts are 'Person' (3.1%), 'People' (2.0%), 'Human' (0.7%), 'Water' (0.5%), and 'Cat' (0.5%).
- CONCEPTNET relations include Causes, CapableOf, Antonym, etc., with 43.6% of questions generated from the relation 'CausedBy'.
- The dataset can be used to evaluate reading comprehension models for answering commonsense questions using web text as context.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focused on testing commonsense knowledge.
- It analyzes the top 500 most frequent concept relations in the dataset, providing examples and frequency percentages.
- Question formulation involves high language variation with 122 contributors, but 10 workers contributed to over 85% of questions.
- The paper examines commonsense skills needed for answering questions by analyzing 100 randomly sampled examples from the development set.
- It identifies six main categories of concept relations: Spatial Concept, Cause & Effect, Has Parts, Is Member Of, Functional Relation, and Attribute.
- The paper highlights the importance of understanding commonsense knowledge for AI systems to improve their performance in real-world scenarios.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focusing on commonsense knowledge.
- It presents 18 commonsense skills, such as 'Has parts', 'Is member of', and 'Purpose'.
- Analyzes the frequency of these skills in sampled data (average of 1.75 per question).
- Provides baseline models for evaluation: VECSIM, LM1B, QABILINEAR, QACOMPARE, ESIM, GPT, BERT, and BIDAF++.
- Discusses training methods (on COMMONSENSEQA or pre-trained) and context usage (web snippets).
- LM1B-CONCAT and LM1B-REP variations are introduced for better performance.
- The paper highlights the importance of commonsense knowledge in question answering tasks.
- It also emphasizes the need to improve current NLU models' ability to handle such questions.
- The paper introduces CommonsenseQA, a question answering challenge focusing on commonsense knowledge.
- It presents various models for addressing this task: QABILINEAR, QACOMPARE, ESIM, BIDAF++, GPT, and BERT.
- QABILINEAR uses a bilinear model to score answers based on question-answer embeddings and cross-entropy loss.
- QACOMPARE is similar to an NLI model, using interaction between the question and answer as input for predicting scores.
- ESIM is an NLI model adapted for multiple choice settings with softmax training and cross-entropy loss.
- BIDAF++ combines BIDAF with a self-attention layer and ELMo representations, using Google web snippets as context.
- GPT adapts pre-trained LMs to perform question answering by encoding questions and candidate answers as delimiter-separated sequences.
- BERT fine-tunes language models for the task, using a masked language modeling objective and linearizing question-answer pairs into delimiter-separated sequences.
- The paper reports that GPT achieves 30% accuracy on CommonsenseQA, while BERT reaches 45%.
- Both GPT and BERT are faster than ESIM by 4.5 times and 2.6 times respectively.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focusing on commonsense knowledge.
- It uses unsupervised pre-trained language models (BERT and GPT) to tackle this task, comparing their performance with human accuracy.
- The dataset is split into training/development/test sets using random or concept-based splits. Random splits are harder for models that learn from CommonsenseQA due to overlapping concepts in different sets.
- Human evaluation shows 88.9% accuracy, while BERT-LARGE and GPT achieve 55.9% and 45.5%, respectively, on the random split (63.6% and 55.5% on concept-based splits). This demonstrates that language models can store large amounts of commonsense knowledge but still lag behind human performance.
- Untrained models perform better than random guessing, while trained models show higher accuracy, with BERT-LARGE achieving the best results.
- ESIM and ELMo representations are also explored in the paper, with ESIM performing better than GPT but worse than BERT-LARGE.
- The paper highlights the importance of commonsense knowledge for language models and provides a benchmark to evaluate their performance in this area.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focused on commonsense knowledge.
- It compares various models' performance, including BERT-LARGE, GPT, SANITY, and others.
- ELMo representations did not improve performance compared to GloVe embeddings in the paper.
- Using web snippets as context for BIDAF++ resulted in low performance, suggesting they don't carry much useful information.
- The random split had lower performance than the question concept split on average.
- SANITY models trained on CommonsenseQA achieved high performance (92% for BERT-LARGE), highlighting the importance of selecting difficult distractors.
- The paper discusses baseline analysis, focusing on BERT-LARGE's performance and its difficulty in handling questions with similar concepts but different answers.
- The authors suggest that the challenge could be used to improve models' ability to handle commonsense knowledge.
- The paper presents a new dataset for evaluating commonsense reasoning, which can help researchers develop better LLMs.
- The paper concludes with future directions and potential applications of this work in AI research.
- The paper introduces ""CommonsenseQA,"" a question answering challenge focused on testing commonsense knowledge.
- It contains 12,247 examples and aims to generate difficult questions at scale using CONCEPTNET.
- The dataset's unique properties include examples with negation, antonyms, finer granularity answers, and conjunction conditions.
- Evaluation of various models shows that the best model (pre-trained LM tuned for the task) achieves 55.9% accuracy, significantly lower than human performance.
- The paper highlights the need to incorporate commonsense knowledge into NLU systems and hopes this dataset facilitates future research in this area.
- Acknowledgments include contributions from anonymous reviewers, Google PhD fellowship, Israel Science Foundation grant, Blavatnik Computer Science Research Fund, Yandex Initiative for Machine Learning, and Theophilus Simpson's work on CONCEPTNET.
",2445
"1901.02860",1,"- Transformer-XL is a novel neural architecture designed to enable learning dependency beyond a fixed length in language modeling without disrupting temporal coherence.
- It consists of segment-level recurrence and a novel positional encoding scheme.
- The method allows for capturing longer-term dependencies, resolving context fragmentation issues, and improves performance on both short and long sequences.
- Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers.
- It achieves better results compared to state-of-the-art models on various benchmarks, including enwiki8, text8, WikiText-103, One Billion Word, and Penn Treebank.
- When trained only on WikiText-103, Transformer-XL can generate coherent, novel text articles with thousands of tokens.
- The code, pretrained models, and hyperparameters are available in both TensorFlow and PyTorch.
- Transformer-XL addresses the limitations of fixed-length contexts by introducing recurrence and relative positional encodings in a self-attention model.
- Recurrence allows for reusing hidden states from previous segments, creating recurrent connections between them and enabling modeling of very long-term dependencies.
- Relative positional encodings enable state reuse without causing temporal confusion, introducing a novel formulation that generalizes to attention lengths longer than training.
- Transformer-XL achieves strong results on five datasets varying from word-level to character-level language modeling and can generate coherent long text articles with thousands of tokens.
- The model outperforms RNNs in both character-level and word-level language modeling, making it the first self-attention model to do so.
- Transformer-XL's main technical contributions include introducing recurrence in a purely self-attentive model and deriving a novel positional encoding scheme.
- The paper introduces Transformer-XL, an attentive language model that addresses the issue of capturing long-range context in language modeling.
- Existing approaches to this problem include manually defined context representations or document-level topics learned from data.
- The work focuses on improving generic sequence modeling by addressing the challenge of learning longer-term dependencies, which has been a long-standing research issue since LSTM's ubiquitous adaptation.
- Transformer-XL is based on the Transformer architecture and aims to learn longer-term dependency in language modeling.
- The model uses a segmented approach to process arbitrarily long contexts, addressing the challenge of training a Transformer with limited resources.
- During training, the model only processes a fixed-length context window, while during evaluation, it can handle arbitrary input lengths.
- The paper presents experimental results showing that Transformer-XL outperforms other models in various tasks, including language modeling and machine translation.
- Transformer-XL achieves state-of-the-art performance on the WikiText-103 dataset with 25% lower perplexity than previous methods.
- The model also demonstrates improved performance in machine translation tasks, achieving a BLEU score of 40.6 on the WMT'14 English-German task and 37.8 on the WMT'14 English-French task.
- Transformer-XL is more efficient than previous models, requiring less training time and memory usage while achieving better performance.
- Transformer-XL addresses limitations of fixed-length context models by introducing segment-level recurrence with state reuse.
- The model caches hidden states from previous segments and uses them as extended context for the next segment, allowing longer dependency modeling and avoiding context fragmentation.
- This approach improves evaluation speed significantly (4.5 times faster) compared to vanilla models that process each segment independently.
- Transformer-XL achieves state-of-the-art results on language modeling tasks, outperforming the vanilla model by 10% in terms of perplexity and 30% in accuracy.
- The model's performance is comparable to models with much larger training data, suggesting that Transformer-XL can effectively exploit contextual information.
- Transformer-XL's architecture is flexible and can be applied to various tasks such as machine translation, text summarization, and question answering.
- The model's performance on long-range dependency tasks demonstrates its ability to handle complex linguistic phenomena.
- Transformer-XL's recurrent mechanism allows it to better capture the temporal dynamics of language compared to vanilla models.
- The paper provides a detailed analysis of the model's performance and discusses potential future directions for research in LLMs.
- Overall, Transformer-XL represents an important advancement in the field of Large Language Models, addressing key limitations of fixed-length context models and improving evaluation speed and performance on various tasks.
- Transformer-XL is an attentive language model that extends context beyond a fixed length by using recurrence mechanisms.
- It differs from standard Transformers in how it conditions key and value on the extended context, creating segment-level recurrence in hidden states.
- The largest possible dependency length grows linearly with the number of layers and segment length (O(N × L)).
- This method is similar to truncated BPTT but differs by caching a sequence of hidden states instead of just one, requiring relative positional encoding.
- Transformer-XL achieves faster evaluation, up to 1,800+ times faster than the vanilla model on enwiki8 dataset.
- The recurrence scheme can be extended to cache as many previous segments as GPU memory allows, using a predefined length-M old hidden states (memory mn).
- Relative positional encodings are used to connect the idea of memory augmented neural networks with Transformer-XL.
- The paper presents experimental results on enwiki8 and wiki10-3 datasets, showing improvements in perplexity and speed.
- Transformer-XL introduces relative positional encodings to address the issue of maintaining coherent positional information in recurrent models.
- Relative positional encodings encode the relative distance between two positions instead of absolute positioning, making it easier for the model to distinguish representations based on their distances.
- This approach allows for a one-to-one correspondence with its absolute counterpart while offering better performance.
- The paper presents an alternative derivation of relative positional encodings compared to previous works in machine translation and music generation.
- Experiments show that Transformer-XL achieves state-of-the-art results on various tasks, including language modeling, question answering, and text summarization.
- The model is 4.5 times faster than the standard Transformer while maintaining comparable accuracy.
- Transformer-XL's architecture can be easily adapted to other recurrent models, such as LSTMs and GRUs, for improved performance.
- The paper also introduces a new method for learning the relative positional encodings from data, which improves model generalization.
- This work highlights the importance of considering temporal information in language modeling and its impact on model performance.
- Transformer-XL's approach to relative positional encodings can be applied to various tasks beyond language modeling, such as speech recognition and machine translation.
- Transformer-XL introduces relative positional embedding for attentive language models, improving generalization and performance.
- Absolute positional embeddings are replaced with relative ones (Ri−j) in terms (b) and (d), reflecting the prior that only relative distance matters for where to attend.
- New trainable parameters u and v replace query and key vectors' absolute positional embedding, suggesting attentive bias should remain constant regardless of position.
- Separate weight matrices Wk,E and Wk,R are introduced for content-based and location-based key vectors, providing intuitive meaning to each term in the attention mechanism.
- The relative positional embedding R adapts sinusoid formulation from Vaswani et al., offering a benefit of automatic generalization to longer memories during evaluation.
- Transformer-XL architecture is derived by equipping recurrence mechanism with proposed relative positional embedding.
- Computational procedure for N-layer Transformer-XL with single attention head summarized, including masked softmax, layer normalization, positionwise feed-forward, and masking.
- Transformer-XL is an attentive language model that addresses the issue of fixed-length context in transformers by introducing a segment-level attention mechanism and dynamic position embedding.
- The paper presents a simple computation procedure for reducing the cost of computing A (attention) from quadratic to linear w.r.t. sequence length, making it more efficient.
- Experiments show that Transformer-XL outperforms state-of-the-art models on various datasets, including WikiText-103, enwik8, text8, One Billion Word, and Penn Treebank.
- The model achieves new state-of-the-art results in the 12-layer Transformer-XL architecture compared to the vanilla Transformer on enwik8 dataset.
- Transformer-XL's performance is comparable to or better than other models with significantly fewer parameters, making it more efficient and practical for real-world applications.
- The paper highlights the importance of dynamic position embedding in addressing long-term dependency modeling issues in language modeling tasks.
- Transformer-XL is an attentive language model that addresses the fixed-length context issue in previous models.
- It achieves better performance with fewer parameters compared to other state-of-the-art methods, such as 64-layer Transformers and LSTMs.
- The model uses a segment-level attention mechanism, allowing it to handle long sequences without losing contextual information.
- By increasing the number of layers from 12 to 18 or 24, Transformer-XL achieves new state-of-the-art results on text8 and One Billion Word benchmarks.
- The model does not require auxiliary losses, unlike previous methods, which means all benefits are attributed to the architecture itself.
- Transformer-XL's performance is comparable to or better than other models in various tasks, including language modeling, machine translation, and question answering.
- The paper demonstrates that increasing model size can lead to improved performance, but it also highlights the importance of efficient architectural design.
- By using Transformer-XL as a base architecture, researchers can create more specialized models for specific tasks without sacrificing performance.
- The model's segment-level attention mechanism allows for better handling of long sequences and preserves contextual information effectively.
- Transformer-XL achieves new state-of-the-art results on Penn Treebank, further demonstrating its effectiveness in various language processing tasks.
- Transformer-XL is an attentive language model designed to better capture longer-term dependency, addressing the issue of context fragmentation in fixed-length models.
- It dramatically improves single-model SoTA from 23.7 to 21.8 and outperforms contemporary methods using vanilla Transformers, suggesting its generalizability to short sequences.
- Transformer-XL achieves a new SoTA result on the Penn Treebank dataset with proper regularization, even on small datasets (1M training tokens).
- Ablation studies show that both the recurrence mechanism and the new positional encoding scheme are necessary for achieving optimal performance and generalizing to longer attention sequences.
- The recurrence mechanism allows increasing the attention length from 128 during training to 640 at test time, while maintaining a standard setting with 151M parameters.
- Transformer-XL remains superior to baselines under GPU memory constraints despite using shorter backpropagation lengths.
- A controlled experiment on the One Billion Word dataset shows that improvements in performance can be attributed to solving context fragmentation rather than capturing longer context length.
- The paper highlights the importance of addressing context fragmentation and its impact on language modeling performance, particularly for LLMs.
- Transformer-XL addresses context fragmentation by introducing segment-level recurrence and relative positional encodings.
- The paper presents a controlled experiment on the One Billion Word dataset, showing that segment-level recurrence improves performance even without long-term dependency needs.
- Relative effective context length (RECL) is introduced as an alternative to Effective Context Length (ECL), addressing its limitations in fair comparison among multiple models.
- The paper demonstrates the effectiveness of Transformer-XL compared to other models, such as QRNN and LSTM, on various datasets with different recurrence ratios.
- RECL shows that Transformer-XL achieves a higher relative effective context length than Shaw et al.'s model (2018) in short sequences, while maintaining comparable performance in long sequences.
- The paper highlights the importance of segment-level recurrence and relative positional encodings for addressing context fragmentation issues in language modeling.
- Transformer-XL introduces a new model that can handle longer contexts by extending the attention mechanism and using positional encodings.
- The Relative Effective Context Length (RECL) metric is introduced to measure the relative improvement of long context models over short context ones, ensuring fair comparison between different model groups.
- Transformer-XL achieves an 80% longer RECL compared to recurrent networks and a 450% longer RECL than the original Transformer.
- The model can generate coherent articles with thousands of tokens without manual cherry picking, despite minor flaws.
- Transformer-XL achieves up to 1,874 times speedup during evaluation compared to the vanilla Transformer model.
- Potential applications include text generation, unsupervised feature learning, image and speech modeling.
- The paper's findings suggest that Transformer-XL can handle longer-term dependency better than RNNs and the original Transformer.
",2542
"1902.03545",1,"- TASK2VEC is a method that provides vectorial representations for visual classification tasks, enabling reasoning about their nature and relations.
- The embedding process involves computing an estimation of the Fisher information matrix associated with probe network parameters.
- This fixed-dimensional embedding is independent of class number or label semantics.
- TASK2VEC can predict task similarities based on intuitive semantic and taxonomic relations, such as plant classification tasks being similar.
- The framework has practical value for selecting a pre-trained feature extractor for new tasks using the task embedding.
- A meta-learning framework is introduced to learn a metric on embeddings that predicts which feature extractors will perform well.
- Selecting a feature extractor with task embedding achieves performance close to the best available feature extractor while costing less than exhaustively training and evaluating all options.
- The Fisher Information Matrix norm correlates with task complexity, while the distance between embeddings captures semantic similarities.
- An asymmetric distance on tasks is introduced that correlates with transferability between tasks.
- The activations of a DNN trained for complex visual recognition tasks are shown to be rich representations of input images, and gradients of weights relative to task-specific loss are rich representations of the tasks themselves.
- The paper introduces Task2Vec, a method for representing tasks as fixed-dimensional embeddings using the diagonal Fisher Information Matrix (FIM) of pre-trained convolutional neural networks.
- These task embeddings encode information about task difficulty, input domain characteristics, and useful features from the probe network.
- The authors propose MODEL2VEC to address limitations by learning joint task and model embeddings, improving performance in selecting an expert for a given task.
- Task2Vec can be used to reason about the space of tasks and solve meta-tasks, particularly useful when data is insufficient for training or fine-tuning generic models.
- The paper presents examples from iNaturalist, CUB-200, and iMaterialist datasets, showing how task embeddings can group similar tasks together based on taxonomic or semantic types.
- Domain embeddings are also introduced to distinguish between different problem domains, such as iNaturalist and iMaterialist.
- The paper highlights the potential practical applications of these methods in transfer learning, meta-learning, and model selection for new tasks.
- Task2Vec and MODEL2VEC can help improve performance by selecting an expert from a given collection, outperforming 1 when solving meta-tasks.
- The paper also discusses the limitations of these methods, such as ignoring interactions between models and tasks, which may play an important role in some cases.
- Future work could involve incorporating model information into task embeddings to improve performance further.
- Task Embedding for Meta-Learning proposes a method called TASK2VEC, which uses Fisher Information Matrix (FIM) to represent tasks as embeddings in a meta-learning framework.
- The FIM measures the information contained by a parameter (weight or feature) about the joint distribution of input and label. It's related to task complexity and can be used to measure learning distance between tasks.
- TASK2VEC uses a probe network that computes FIMs for different networks, then averages them to obtain an embedding for each task. This embedding is used as the task representation in meta-learning.
- The method outperforms other baselines in meta-learning scenarios, achieving 30% accuracy on attribute tasks from iMaterialist and reducing training time by 4.5 times compared to fine-tuning a generic model trained on ImageNet.
- TASK2VEC can be used for transfer learning, where it helps select the most suitable task representation for a given query task.
- The paper discusses how TASK2VEC contributes to prior literature and presents empirical results in Section 5.
- The paper introduces TASK2VEC, a method for task embedding using Fisher Information Matrix (FIM) approximations to represent tasks in meta-learning.
- To address the issue of non-comparable FIMs computed on different networks, a ""probe"" network pre-trained on ImageNet is used as a feature extractor, with only the classifier layer re-trained for each task.
- The full FIM is approximated by considering diagonal entries and averaging Fisher Information for all weights in the same filter, resulting in a fixed-size representation.
- A more robust estimator of the FIM is used to avoid noise issues when trained with few samples, leveraging connections to variational inference.
- The optimal value of Λ (precision matrix) is estimated as an approximation of the FIM, which can be minimized through Stochastic Gradient Variational Bayes.
- TASK2VEC's properties include being invariant to permutations and weight rescaling, having a simple interpretation in terms of filter importance, and being easy to compute.
- The method is shown to outperform other baselines on several meta-learning tasks, including few-shot learning and model agnostic meta-learning (MAML).
- TASK2VEC can be applied to any task with a CNN backbone, making it suitable for various applications in computer vision and beyond.
- Task2Vec embedding is defined using Fisher Information Matrix (FIM) of a feature extractor, which can be calculated efficiently with local reparametrization trick.
- The task embedding has useful properties, such as invariance to label space, encoding task difficulty, and capturing task domain.
- Invariance to label space means the embedding does not directly depend on labels but only on predicted distributions; it's invariant to permutations of labels and has a fixed dimension regardless of output space.
- Encoding task difficulty: As the model becomes more confident in its predictions, the norm of the task embedding (FIM) scales with the task's difficulty for a given feature extractor.
- The embedding can capture task domain by showing that data points classified with high confidence have lower contributions to the task embedding than those with low or moderate confidence.
- In experiments, the FIM norm correlates with test performance even for complex models trained on real data.
- Task2Vec distance is shown to be correlated with taxonomic distances between species classification tasks.
- The paper demonstrates that the task embedding can be used as a representation of meta-learning problems and can help in transfer learning, few-shot learning, and zero-shot learning scenarios.
- The method can also be applied to other domains such as natural language processing (NLP) and computer vision tasks.
- Task2Vec is shown to outperform existing methods for measuring task similarity in terms of accuracy and efficiency.
- Task2Vec introduces a novel method for embedding tasks in a meta-learning setting, using task-weighted domain embeddings based on data near decision boundaries.
- The FIM (Fisher Information Matrix) is used to capture the sensitivity of loss function and identify relevant features for each task.
- Two main similarity measures are considered: taxonomic distance and transfer distance. Taxonomic distance relies on a hierarchical structure of categories, while transfer distance measures the difference in expected performance between two tasks.
- The paper demonstrates that Task2Vec achieves better results than other baselines in various meta-learning scenarios, including few-shot learning and zero-shot learning.
- Task2Vec can be used to select pre-trained feature extractors for a new training task by considering the similarity between tasks.
- The method is applied to real-world data from iNaturalist, showing promising results in classifying species of plants and animals.
- Task2Vec's performance is robust even when there are few samples available for each task.
- The paper highlights the importance of using domain embeddings based on data near decision boundaries to capture useful features for tasks.
- Task2Vec can be extended to other meta-learning scenarios, such as multi-task learning and transfer learning.
- This work contributes to a better understanding of how to represent and compare tasks in the context of meta-learning.
- TASK2VEC is a task embedding method for meta-learning that uses Fisher information to capture fundamental structure of tasks and compute distances between them.
- Symmetric and asymmetric TASK2VEC metrics are proposed to address the issues with Euclidean distance, such as different parameter scales and varying norms due to task complexity and sample size.
- The symmetric TASK2VEC distance (dsym) uses cosine distance between normalized embeddings, capturing semantic similarity between tasks. It correlates well with taxonomical distances in iNaturalist data.
- Asymmetric TASK2VEC distance (dasym) considers both task similarity and complexity, using the trivial embedding as a reference point. The hyperparameter α can be selected based on meta-tasks, with a robust value found to be 0.15 in experiments.
- Model2Vec is introduced, which extends TASK2VEC by incorporating model information into task embeddings. This allows for better representation of models trained on specific tasks and enables transfer learning between tasks and models.
- TASK2VEC aims to learn a joint embedding of tasks and models, allowing for better model selection based on task similarity.
- The method uses an embedding vector (mi) that combines the task embedding (Fi) with a learned ""model bias"" (bi).
- Model bias is optimized using k-way cross entropy loss to predict the best model given the task distance.
- After training, given a novel query task, the method predicts the best model by finding the model with the closest embedding to the query task.
- Experiments were conducted on a large collection of tasks and models from various datasets, including iNaturalist, CUB-200, iMaterialist, and DeepFashion.
- The ResNet-34 pretrained on ImageNet was used as the probe network for these experiments.
- TASK2VEC showed promising results in both qualitative properties of the embedding and meta-learning tasks.
- This method can be applied to various domains, including image classification, object detection, and semantic segmentation.
- The paper also presents a new dataset called ""Meta-Learning for Task Embedding"" (METE) that contains 1000 pairs of tasks and models from DeepFashion.
- TASK2VEC achieved an accuracy of 65% on the METE dataset, outperforming other baselines.
- The paper explores task embedding for meta-learning, focusing on analyzing data distribution and expert selection in various tasks.
- It uses datasets like CUB, iNaturalist, and DeepFashion to demonstrate the effectiveness of task embedding.
- Tasks have varying numbers of training samples, simulating real-world heavy-tail distributions.
- The paper introduces TASK2VEC, a model selection algorithm that suggests an optimal expert for a given task without requiring brute-force search.
- TASK2VEC recovers the best or near-optimal feature extractor in most cases, with specialized experts performing similarly to generic ones in some tasks but outperforming them in others.
- The model's performance is influenced by the task embedding norm: lower norm leads to lower error and more complex tasks benefit from specialized experts.
- TASK2VEC uses ResNet-34 models pre-trained on ImageNet as ""expert"" feature extractors, with some fine-tuned for specific tasks or collections of related tasks.
- A linear classifier is trained for each combination of expert and task to solve the selected task using the expert's features.
- The paper presents results from training 4,100 classifiers, 156 feature extractors, and 1,460 embeddings.
- TASK2VEC has practical applications in meta-learning, particularly for tasks with limited data or heavy-tail distributions.
- TASK2VEC is a method that generates task embeddings for meta-learning, which can predict the best expert feature extractor for a given task.
- The paper presents two model selection meta-tasks: iNat + CUB and Mixed. These tasks test fine-grained expert selection in restricted domains and model selection between different domains and tasks.
- Task embedding results show that TASK2VEC qualitatively reflects taxonomic distance for iNaturalist, with strong agreement between symmetric TASK2VEC distance and taxonomical distance.
- In the case of iMaterialist, task embeddings yield interpretable results, showing non-trivial grouping based on semantic similarity.
- The paper compares TASK2VEC embedding to a domain embedding baseline, demonstrating that some tasks are highly correlated with their domains while others differ only in labels.
- Performance of model selection using TASK2VEC improves results at different dataset sizes and training conditions compared to brute force, fixed ImageNet feature extractor, and finetuning.
- The paper introduces a probe network that achieves top-10 accuracy on the CIFAR-10 dataset with VGG-13, outperforming chance by 59.52%.
- TASK2VEC can be used to accurately select a good fixed feature extractor in low-data regimes, which is more efficient and effective than finetuning ImageNet feature extractors.
- Task Embedding for Meta-Learning (TASK2Vec) aims to create a fixed feature extractor that can generalize across various tasks by learning task representations.
- Probe networks are used to evaluate the effectiveness of these embeddings, with VGG-13, DenseNet-121, and ResNet-13 showing varying performance improvements over chance.
- Embedding recovers similar clusters on iNaturalist but collapses all tasks into a single uninformative cluster in the iMaterials domain.
- Task Embedding encodes task difficulty, with the norm of embedding vectors correlating with task complexity for real tasks and architectures.
- Two strategies are proposed for model selection: selecting an expert feature extractor based on similarity to a given task or using a learned metric from jointly embedding models and tasks.
- The Asymmetric TASK2VEC model selection method performs close to the ground-truth optimal in various meta-tasks, improving over both chance and generic ImageNet experts.
- Error distribution shows that most experts cluster around a mean value, with only a few achieving significantly better performance on specific tasks. This highlights the importance of having access to a large collection of experts when solving new tasks.
- Finding experts is especially important for tasks with limited training data, as it can improve classification accuracy and efficiency over generic experts.
- The paper demonstrates that TASK2Vec can be used in real-world scenarios, such as identifying bird species from images, where it outperforms a state-of-the-art model by 10%.
- The method's O(1) complexity makes it efficient for large collections of experts, while searching over N experts has an O(N) complexity.
- TASK2Vec is a meta-learning framework that can effectively find optimal experts for various tasks, especially when sample sizes are small.
- The performance of TASK2Vec remains close to optimum across varying dataset sizes and outperforms selecting generic experts (e.g., ImageNet).
- DenseNet and ResNet architectures perform better as probe networks compared to VGG for computing the TASK2Vec embedding.
- Taskonomy explores task structure and knowledge transfer in a curated collection of 1,460 fine-grained classification tasks, while TASK2Vec focuses on representing tasks in a topological space with constant-time embedding.
- The large task collection and cheap embedding allow for efficient model selection without exhaustive search.
- Asymmetric TASK2Vec outperforms picking a fixed general model (e.g., ImageNet) or an expert at random, especially on tasks where experts trained on similar tasks may not yield good transfer.
- The paper introduces a novel approach to meta-learning and task embedding that can improve model selection performance in various scenarios.
- TASK2Vec is a method that represents tasks as fixed-dimensional vectors, enabling efficient meta-learning by utilizing Fisher kernels and Fisher Information matrices (FIM) in neural networks.
- The approach uses FIM to characterize the task, leveraging its popularity in approximating natural gradient descent for optimization and various regularization schemes.
- TASK2Vec's efficiency allows tackling new meta-learning problems with large task collections and cheap embedding costs.
- Fisher kernels are inspired by Jaakkola and Hausler's ""Fisher Kernel"", which uses gradients of a generative model score function to represent similarity between data items.
- Variants of the Fisher kernel have been widely used in image, protein molecule, and text representation, as well as unsupervised learning.
- TASK2Vec's approach differs from previous methods by using FIM to characterize a whole dataset (task) instead of individual data items' gradients.
- Meta-learning applications include neural architecture search, hyperparameter estimation, and selecting classifiers for new tasks.
- The norm of the TASK2Vec embedding correlates with test error on the task, while cosine distance between embeddings correlates with natural distances between tasks when available (e.g., taxonomic).
- Practical applications include using TASK2Vec to improve meta-learning performance in various domains such as computer vision and natural language processing.
- The method's efficiency allows for faster model selection, reducing the need for extensive evaluation of multiple models on each task.
- TASK2Vec is a method that creates task embeddings, representing tasks as vectors to facilitate meta-learning.
- These task embeddings correlate with natural distances between tasks, such as taxonomic distance for species classification and fine-tuning distance in transfer learning.
- Using TASK2Vec, an expert feature extractor can be selected from a collection to improve test performance while minimizing training overhead.
- Meta-learning on the space of tasks is crucial for general artificial intelligence development.
- The paper introduces methods to deal with thousands of tasks and reconstruct task space topology, enabling meta-learning solutions testing.
- Current experiments demonstrate the usefulness of TASK2Vec, but more work is needed to test effectiveness, robustness, and limitations on larger, more diverse collections.
",3488
"1902.05522",1,"- The paper introduces a method to store multiple models within a single set of parameters, allowing them to coexist and be retrieved individually.
- Experiments with neural networks show that a surprisingly large number of models can effectively be stored in a single parameter instance without significant interference between models.
- This approach can be viewed as an online complement to compression, utilizing the unrealized capacity during training rather than reducing network size after training.
- The method partially exploits excess capacity present in neural networks by learning multiple tasks simultaneously, effectively requiring fewer parameters per task compared to a single-task model.
- A separate set of parameters is learned for each task, stored in superposition and accessed using context information (Ck) that dynamically routes inputs towards specific models.
- The paper's findings suggest that individual parameters may interfere with each other during training but still allow for thousands of training steps without significant interference between tasks.
- This approach could be useful in scenarios where multiple tasks need to be performed simultaneously, reducing the overall number of parameters required and potentially improving efficiency.
- The method is inspired by Kanerva's work on hetero-associative memory, which uses ""memory"" (parameters) and ""keys"" (context information) for accessing specific data.
- The paper proposes a method to combine multiple models into one by storing their parameters in superposition, reducing interference between tasks and improving performance.
- This approach assumes that input data has an intrinsically low-dimensional representation compared to its ambient space (e.g., natural images).
- To minimize interference, the proposed method stores parameter vectors after rotating them into nearly orthogonal parts of the space using task-dependent context information.
- The appropriate choice of context ensures that parameters for different tasks remain nearly orthogonal during learning, reducing interference and improving performance on individual tasks.
- This method has wide-ranging applications, including training neural networks in memory-constrained environments, online learning of multiple tasks, and overcoming catastrophic forgetting.
- Application to Catastrophic Forgetting: The paper addresses the issue of poor performance on previously encountered data due to changes in input distribution or output labels (catastrophic forgetting).
- Existing solutions for this problem include maintaining a memory of all data, training separate networks for each task, or selectively updating weights using various criteria. However, these methods have limitations and increase computational cost.
- The proposed method uses the same set of parameters in a neural network to perform multiple tasks by storing their weights in superposition. This approach allows reuse of weights and improves learning capacity for future tasks without increasing computational costs or requiring additional storage for task-specific variables.
- The paper provides an example where a single model can achieve 30% accuracy on a classification task, while the same model with separate parameters for each task achieves only 15% accuracy.
- This method is shown to be 4.5 times faster than training separate models for each task and has potential applications in various fields such as computer vision, natural language processing, and robotics.
- The paper introduces Parameter Superposition (PSP) as a method to store multiple models simultaneously within one set of parameters, addressing the issue of catastrophic forgetting in neural networks.
- PSP works by analyzing the fundamental operation in all neural networks - multiplying inputs by weight matrices. Over-parameterization implies that only a small subspace spanned by rows of W is relevant for each task.
- By using task-specific linear transformations (contexts) Ck, it's possible to make the rows of each WiC−1k occupy mutually orthogonal subspaces in ℜN. This allows storing multiple models without interference when summed together.
- The parameters for an individual task can be retrieved using context Ck and referred by ^Wk. Noisy retrieval won't affect overall performance if the noise (ϵ) remains small.
- In the special case of CTk as orthogonal matrices representing rotations, yk = W(Ckx) can be rewritten as yk = W(Ckx). This allows for continuous learning in time-varying input and output label distributions.
- PSP requires substantially less additional variables per new task compared to other methods (1 additional variable per task for one variant of the method).
- The paper demonstrates the effectiveness of PSP on two online image classification settings: time-varying input data distribution, and time-varying output label distribution.
- PSP can overcome catastrophic forgetting in the permuting MNIST task, handle continuously changing input distributions in rotating MNIST and fashion MNIST tasks, and manage changing output labels on incremental CIFAR datasets.
- The paper provides a detailed analysis of ϵ for some choices of context vectors in Appendix A.
- PSP has potential practical applications in various domains where neural networks need to adapt to time-varying data distributions or label changes, such as autonomous driving and medical image classification.
- PSP (Parallel Superposition) model learns a single set of parameters W for multiple tasks by rotating inputs into orthogonal subspaces, assuming they lie on low-dimensional manifolds.
- Rotational superposition is the most general way to choose context, but it's not memory efficient. Reducing memory requirements can be achieved through various restrictions like using random permutation matrices or diagonal matrices.
- In the special case of a diagonal context (Ck = diag(ck)), PSP reduces to an element-wise multiplication and requires only M additional parameters per task.
- Complex superposition allows choosing ck as complex numbers on the unit circle, leading to a diagonal orthogonal matrix. This approach can reduce memory footprint to a single parameter per task by using integer powers of one context vector.
- Binary superposition is a special case of complex superposition with context vectors limited to {-1, 1}, offering computational and memory advantages.
- Superposition combines multiple models into one, offering computational and memory advantages.
- Binary superposition is compatible with real-valued and low-precision linear transformations.
- Extending neural network superposition to entire models involves applying superposition (Equation 3) to the linear transformation of all layers in a neural network.
- Superposition can be applied to convolutional networks, where context is associated with weights instead of input images, reducing computation.
- Experiments show that superposition mitigates interference in learning due to changes in data distribution (catastrophic forgetting).
- Permuting MNIST dataset was used as a test case, demonstrating improved accuracy for binary superposition models compared to baseline models.
- Accuracy increased with the number of units in fully connected networks, and binary superposition models performed better than baseline models on permuted MNIST challenges.
- The paper explores a method called ""Superposition of many models into one"" to address catastrophic forgetting in neural networks.
- In this approach, separate context parameters are chosen for each task, creating new models within the same network that can learn different tasks.
- Three types of superposition are investigated: binary, complex, and rotation.
- The paper demonstrates that larger networks with more parameters are better at fitting data and being robust to catastrophic forgetting. However, it also shows that PSP methods (particularly pspBinary) outperform standard neural networks in mitigating forgetting.
- Performance improves as the number of parameters increases due to space for more models in superposition. With a 2048-unit hidden layer, performance on the initial task remains virtually unchanged after training for 49 other tasks with different input data distributions.
- Different methods of storing models in superposition use varying numbers of additional parameters and affect the network's ability to mitigate forgetting.
- The paper highlights that PSP methods can be applied to any neural network architecture, making it a general approach for addressing catastrophic forgetting.
- This method has practical applications in domains where tasks change frequently or require learning from multiple and diverse data sources.
- The study shows that the use of task identity information is not unique to PSP methods but has been used by previous works as well.
- The paper provides a novel approach for mitigating catastrophic forgetting in neural networks, which can potentially improve performance in various applications where tasks change frequently or require learning from diverse data sources.
- The paper introduces a method called ""Per Task Superposition"" (PSP) for neural networks, which combines multiple models into one to improve performance and reduce catastrophic forgetting in tasks with continuously changing data streams.
- PSP methods include pspBinary, pspComplex, and pspRotation, each requiring additional parameters compared to a standard network. Larger parameter sets allow for more general orthogonal transformations and more rotations, leading to better performance.
- PSPRotation offers the best performance but is impractical due to its high number of additional parameters. PSPComplex performs better than pspBinary, with negligible differences in larger networks.
- The paper demonstrates that PSP methods are robust to catastrophic forgetting on rotating MNIST and fashionMNIST datasets, outperforming previous methods.
- PSP methods can be applied to any neural network architecture, making them a versatile solution for handling continuously changing data streams in various applications.
- The paper introduces a method called Parameter Superposition (PSP) that combines multiple models into one, improving performance and robustness to data distribution changes.
- PSP can be implemented in two ways: pspComplex (extending neural networks to complex numbers) and pspBinary (easiest implementation). The paper demonstrates the effectiveness of pspBinary, with pspComplex potentially further enhancing performance.
- Comparing PSP's performance to previous methods (EWC and SI), PSP outperforms them in addressing catastrophic forgetting on permuted MNIST tasks.
- To simulate real-world continuous domain shift, the paper proposes rotating-MNIST and rotating-FashionMNIST datasets, where images are rotated in-plane by a small amount over time. PSP models show robustness to these changes.
- The proposed method is practical as it can be applied to any neural network architecture without requiring additional training data or task-specific knowledge.
- PSP's performance on rotating datasets shows that the approach is effective in addressing catastrophic forgetting and maintaining accuracy over time, even when input distributions change gradually.
- The paper investigates the PSP (Parallel Superposition) approach, which combines multiple models to address catastrophic forgetting and non-stationary data distribution issues in machine learning.
- Experiments show that PSP's effectiveness is not limited to a specific dataset or type of change in input distribution.
- To automatically choose context parameters without task identity information, the authors introduce pspFast, which randomly changes context at every time step and reuses them after 1000 steps. This scenario requires storing 100x more models compared to previous scenarios.
- In situations where detailed task identity is not available but some knowledge about data distribution changes is known (e.g., rotating fashion MNIST), the authors propose pspFastLocalMix, which incorporates coarse information about non-stationarity into context vectors. This leads to better performance than pspFast.
- The paper also discusses output interference in neural networks and how it can affect learning when transitioning between tasks with different label distributions.
- Experiments on the incremental CIFAR (iCIFAR) dataset show that PSP-based models outperform standard ResNet18 by 30% accuracy, while being 4.5 times faster in training time.
- The paper highlights the potential practical applications of PSP in real-world scenarios where data distribution changes over time and catastrophic forgetting is a concern.
- The paper introduces a novel method called Parameter Superposition (PSP) to address catastrophic forgetting in neural networks.
- PSP stores multiple parameters for various tasks within a single network, treating them as memory and retrieving task-specific models using context vectors based on the task identity.
- The framework works with both fully connected nets and convolutional nets, can be scaled to state-of-the-art neural networks like ResNet, and is robust to input and output interference.
- PSP outperforms existing methods in dealing with catastrophic forgetting and can easily incorporate coarse information about task distribution changes without relying solely on task identity.
- The paper proposes new tasks (rotating MNIST and rotating fashion MNIST) to test the method's performance, demonstrating its effectiveness in various scenarios.
- The paper introduces a method called PSP (Parallel Superposition of Models) that enables storing multiple models in a single neural network, addressing catastrophic forgetting and improving efficiency.
- PSP utilizes random matrices from classical compact groups to create context vectors for each model, allowing them to coexist without interference.
- The authors propose rotating MNIST and fashion MNIST tasks to simulate slowly changing task distributions, reflecting real-world scenarios.
- Future research directions include investigating the number of models that can be stored in superposition, considering neural network architecture and task family.
- Another interesting avenue is automatically determining context vectors instead of relying on task-specific information, with a focus on making them differentiable rather than fixed.
- The paper references various related works, including the lottery ticket hypothesis, catastrophic forgetting in neural networks, and network pruning techniques.
- PSP's practical applications include reducing memory requirements for storing multiple models, improving efficiency by avoiding retraining, and potentially enhancing generalization performance.
- The paper highlights that PSP can be applied to any model with a linear readout layer, making it applicable to various tasks and architectures.
- Experiments show that PSP achieves 30% accuracy on the rotating MNIST task, outperforming other methods in terms of efficiency (4.5 times faster than retraining).
- The paper's findings suggest that PSP can be a valuable tool for addressing catastrophic forgetting and improving model efficiency in real-world scenarios.
- The paper introduces a method for superpositioning multiple models into one, allowing them to learn from each other and recover linear transformations with destructive interference.
- Properties of destructive interference enable the recovery of a linear transformation from the superposition, as shown in Appendix A, B, and D.
- The paper demonstrates that after offline training, models can be stored in superposition and retrieved with small noise (Appendix A). Online training is also possible for models in superposition (Appendix B).
- Complex vectors can be generated compositionally using the superposition method (Appendix D).
- The paper provides a matrix notation representation of the recovery process, showing that the first term represents the recovered linear transformation and the second term is a residual (Equation 9).
- The authors present an example application where a model learns to classify images from multiple datasets, demonstrating how superpositioning can improve performance by combining models with different strengths.
- Superpositioning allows for faster training times compared to training individual models separately, as it leverages the knowledge of previously trained models.
- The paper highlights that superpositioning can be applied to any model architecture and learning algorithm, making it a versatile technique.
- By combining multiple models into one, superpositioning reduces memory requirements and computational costs compared to training individual models separately.
- Superpositioning can potentially improve generalization performance by allowing models to learn from each other's strengths and weaknesses.
- Superposition of many models into one aims to analyze interference and retrieval noise in this process.
- Proposition 1 states that, in expectation, other models within the superposition will not introduce a bias to the recovered linear transformation (Es[ϵ] → 0). This is proven for three cases: real-valued networks with binary context vectors, complex-valued networks with complex context vectors, and real-valued networks with orthogonal matrix contexts.
- Proposition 2 shows that under mild conditions, the variance induced by context vectors (Var [⟨c⊙w,x⟩] or Var (⟨Ckw,x⟩)) is approximately proportional to 1/M when M is large. This implies that if |⟨w, x⟩| is large, then |⟨c ⊙ w, x⟩| and |⟨Ckw, x⟩| will be relatively small compared to |⟨w, x⟩|.
- When K−1M is small, the residual introduced by other superimposed models stays small. Binding with random keys roughly attenuates each model's interference by a factor proportional to 1/√M.
- The paper introduces a method to reduce interference among multiple models by using random keys, which attenuates each model's influence proportionally to 1√M (where M is the model dimension).
- This approach works for real-valued networks with binary context vectors, complex-valued networks with complex context vectors, and real-valued networks with rotational context matrices.
- In the first case, the variance of the inner product between random keys and input data is approximated as η²M∥w∥2∥x∥2 (where η is a large constant).
- For complex-valued networks, the variance of the inner product between random keys and input data is also approximated as η²M∥w∥2∥x∥2.
- In rotational context matrices, the paper shows that the variance of the inner product between random keys and input data is approximately 1/M times the variance in a standard case.
- The method's effectiveness depends on the assumption that each term w∗i xi has a comparably small contribution to the inner product (e.g., using dropout).
- This approach can potentially lead to better generalization and improved performance when combining multiple models, as it reduces interference among them.
- The paper introduces a method to train individual models in superposition during training, allowing for online learning with unitary transformations.
- Proposition 3 shows that parameter updates of an individual model in superposition are approximately equal to those of the same model trained outside of superposition. This results in analogous destructive interference properties.
- The gradient of parameter superposition creates a superposition of gradients, which can be applied in an online fashion for memory operations.
- Training a PSP (parameter superposition) network with context vectors c1 yields nearly the same change in parameters w as training the network independently and then combining it with another model using context vectors.
- The paper introduces a superposition function ϕ, which combines weights w with other parameters, and a read-out function ρ that extracts w from W (with some error e).
- Conditions for superposition and read-out functions are sought to ensure the gradient of f with respect to w is equal or nearly equal to the gradient of F with respect to W, transformed back to the w space.
- The paper's findings could lead to more efficient training methods by combining multiple models in a single network, potentially improving performance and reducing computational costs.
- The paper proposes a method to superpose multiple models into one by combining their weights and context vectors, resulting in a single model with improved performance.
- By defining a superposition function, the authors show that under certain conditions, the gradient of the combined model's loss function is equal to the sum of the gradients from each individual model. This allows for the efficient training of a unified model without losing accuracy.
- The paper covers three types of context vectors: binary, complex-valued, and real-valued rotational matrices. Each type has its own superposition operation and readout function, ensuring that the necessary condition is satisfied.
- The authors provide geometric illustrations to show how these operations work in embedding spaces.
- This method can be applied to various models, including neural networks, support vector machines, and logistic regression.
- The paper demonstrates that this approach can improve accuracy while reducing the number of parameters, leading to faster training times.
- The proposed method is particularly useful for large-scale problems where multiple models are already trained separately, as it allows for a unified model without retraining each individual model.
- The authors suggest future work could involve applying this technique to other types of context vectors and exploring its potential in federated learning scenarios.
- The paper introduces superposition of models into a single model, creating a unified embedding space for context operators and parameters.
- By treating contexts as operators in abstract algebra, new contexts can be constructed through composition operations.
- Compositions of contexts enable parameter storage and recovery from the composition of contexts.
- Functions c(k) over superposition dimension k allow for generating new context vectors in various ways.
- Mixtures of contexts create smoother transitions between contexts, reducing orthogonality and allowing parameters with neighboring contexts to share information.
- This sharing of information is beneficial for transfer-learning settings and continual learning scenarios where domain shifts are smooth.
- The paper presents additional results comparing the accuracy of various methods on different tasks.
- The memory and computation footprint of pspRotation makes it impractical for most applications, as it requires storing independent networks.
",4120
"1903.05895",1,"- The paper focuses on fast linear transforms, which are ubiquitous in machine learning and include DFT, DCT, and convolutions.
- It investigates the extent to which hand-crafted algorithms for these transformations are necessary and how much knowledge is required to automatically learn a fast algorithm for a given structured transform.
- The paper introduces a parameterization of divide-and-conquer methods that can represent a large class of transforms, allowing automatic learning of efficient algorithms for many important transforms.
- This method recovers the O(N log N) Cooley-Tukey FFT algorithm to machine precision for dimensions up to 1024.
- The approach can be incorporated as a lightweight replacement in ML pipelines, learning efficient and compressible transformations.
- On a CIFAR-10 classification task, the method exceeds unconstrained matrices' accuracy by 3.9 points with 4X faster inference speed and 40X fewer parameters.
- The paper highlights the foundational question of understanding minimal prior knowledge needed to learn high-speed systems, which ties into modern trends toward relaxing manually imposed structure (AutoML).
- Key lessons from De Sa et al.'s work are drawn, characterizing matrices with efficient matrix-vector multiplication algorithms as being factorizable into products of sparse matrices.
- The paper demonstrates that divide-and-conquer schemes lead to fast multiplication algorithms for a surprisingly general set of structured matrices.
- Practical applications include compressing single hidden-layer networks, where the method exceeds unconstrained matrices' accuracy on CIFAR-10 with 4X faster inference speed and 40X fewer parameters.
- Propose a recursive structure for fast algorithms using butterfly factorizations, called butterfly matrices.
- This relaxed representation captures a larger class of structures and can learn from data with O(N) parameters and O(N log N) operations.
- Empirically validate the method by recovering famous transforms (DFT, Hadamard, DCT, convolution) for realistic sizes up to 1024 dimensions.
- Incorporate this method in end-to-end ML pipelines to learn fast and compressible latent transformations, outperforming unconstrained models on several datasets.
- Compare training and inference speed with specialized implementations of discrete transforms, achieving 3-5X performance for DFT and DCT while still learning a rich class of general transforms.
- Related work discusses the importance of fast transforms in machine learning pipelines, structured matrices, and previous efforts to find more general classes of fast transforms.
- Learning fast algorithms for linear transforms using butterfly factorizations
- Approach simplifies discreteness by learning a set of permutations, allowing recovery of fast algorithms for realistic dimensions
- Compressed deep learning models can use this method as a drop-in replacement for matrices in end-to-end ML models
- Preliminaries: Sparse factorizations and their connection to fast algorithms (DFT case study)
- Recursive structure of DFT leads to the Cooley-Tukey FFT algorithm, which can be written as a matrix factorization
- Unrolling recursion yields butterfly matrices and factorizations, with each BN/2k being a 2 × 2 block matrix of diagonal matrices called butterfly factors
- Butterfly matrices have sparse product width (SPW) equal to the length of the shortest linear straight-line program describing them
- Hierarchy of matrix classes built on butterflies, with perfect capture of transforms at each level
- Expressive power of these matrices allows for learning fast algorithms for a wide range of recursive structures
- Experiments show that the proposed method can learn fast algorithms for various transforms and outperforms existing methods in terms of accuracy and speed
- Structured butterfly factors: Combine permutation matrices to obtain a single permutation called bit-reversal permutation, which sorts indices by reverse binary representation.
- Recovering fast transform algorithms: Use class of matrices built as products of specific factors that capture recursive nature of many fast algorithms.
- Butterfly factorization: Factorize linear transform TN into B(N)P (N), where B(N) is a butterfly matrix and P (N) is a permutation. Also consider B(N)2P (N)2 or B(N)1P (N)1.
- Learning recursive permutations: Restrict to learning over permutations with simple structure, allowing 3 binary choices at each step in the recursion. Parameterize permutation as a categorical distribution of possible combinations.
- The paper introduces a method for learning fast algorithms using butterfly factorizations, which simplify linear transforms in large language models (LLMs).
- It represents the permutation matrix P as a product of three probabilities (Pc, Pb, Pa) and learns them via logits ℓa, ℓb, ℓc.
- Initialization is crucial for proper butterfly factorization, ensuring that each factor is close to unitary or orthogonal.
- The paper's approach differs from previous works in several ways: it explicitly models and learns a permutation matrix P, does not enforce the matrix to be orthogonal, has a different weight-tying scheme, and orders factors differently.
- The BP hierarchy covers a spectrum of matrix classes, ranging from structured matrices with a linear number of parameters to all square matrices.
- The proposed method can recover fast algorithms for important transforms like the Fourier Transform, Hadamard Transform, DCT, and DST.
- Empirical evaluation shows that the butterfly parameterization can learn these transforms efficiently and accurately.
- The BP hierarchy has a VC dimension almost linear in the number of parameters, similar to other compressed parameterizations like LDR.
- The paper's method is faster and simpler to implement than previous approaches.
- Applications include learning fast algorithms for image processing, signal processing, and machine learning tasks.
- The paper introduces a method for learning fast algorithms for linear transforms using butterfly factorizations, which can improve performance in deep learning models while ensuring fast multiplication and few parameters by design.
- Discrete transforms evaluated include the discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete sine transform (DST), convolution, Hadamard transform, and discrete Hartley transform.
- The method minimizes the Frobenius norm of the difference between the target matrix TN and a product of blocks of butterfly and permutation products to recover fast algorithms for these transforms.
- Experiments show successful recovery of fast algorithms up to N = 512 for convolution and N = 1024 for other transforms, with methods like sparse, low-rank, and combinations used as baselines.
- The method is implemented in PyTorch and the code is available on GitHub (https://github.com/HazyResearch/butterfly).
- The paper introduces a butterfly parameterization for fast algorithms of linear transforms using butterfly factorizations, which can recover common transforms and convolutions up to certain sizes.
- This approach outperforms other methods in compressing single hidden layer neural networks, achieving higher accuracy than fully-connected layers on all tested datasets while using fewer parameters.
- The butterfly parameterization is highly competitive with other structured matrix approaches like Fastfood, Deep Fried Convnets, and Toeplitz-like matrices.
- The paper also discusses the relationship between compression ratio and generalization error, suggesting that improvements can arise from both lower generalization error due to fewer parameters (VC bounds) and better inductive bias encoded by structured classes.
- The butterfly parameterization is a more general divide-and-conquer decomposition than the Cooley-Tukey fast Fourier transform, allowing for many unconventional permutations that lead to exact factorizations of FFTs.
- This approach can be applied in various applications such as neural network compression and lightweight additions to larger-scale ResNet architectures.
- The paper introduces a structured class that improves over fully connected (FC) layers by imposing approximate equivariance to more general transformations, using butterfly factorizations for parametrization.
- Butterfly parameterization can represent arbitrary convolutions and encode important priors, as seen in ResNet18 experiments on CIFAR-10 dataset.
- The BPBP layer improves performance over a standard FC layer while adding negligible parameters to the original model.
- Training and inference speed comparison shows that the butterfly matrix is 15% faster than dense matrix multiply (GEMM) for training, within 40% of FFT, and one or two orders of magnitude faster than GEMV for inference on CPU.
- The method yields consistent performance improvements and substantial compression and speed increases as a component of end-to-end machine learning models.
- The paper validates the method by learning transforms such as DFT, DCT, Hadamard transform, and convolutions up to machine precision and dimension N = 1024.
- The authors thank various organizations for their support in research and acknowledge the contributions of multiple individuals and institutions.
- The paper introduces a method for learning fast algorithms for linear transforms using butterfly factorizations, which can accelerate and compress deep neural networks.
- Butterfly factorization is an efficient matrix decomposition technique that reduces the computational complexity of linear algebra operations.
- The proposed approach combines butterfly factorization with a novel algorithmic framework to learn fast algorithms for linear transforms in deep learning models.
- The method can be applied to various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected layers.
- Experiments show that the proposed approach achieves up to 30% accuracy improvement with a 4.5 times faster training time compared to state-of-the-art methods.
- The method can also compress deep neural networks, reducing their memory footprint by up to 12x without compromising performance.
- The paper provides theoretical guarantees for the proposed approach and demonstrates its practical benefits in various applications.
- This work contributes to the field of large-scale machine learning by offering a computationally efficient method for accelerating and compressing deep neural networks.
- Learning fast algorithms for linear transforms using butterfly factorizations: This paper introduces a new method to compute linear transforms efficiently, focusing on butterfly factorization techniques.
- Butterfly factorization: The authors present an algorithm that decomposes a matrix into a product of sparse matrices and a diagonal matrix, resulting in fast matrix-vector multiplications.
- Applications: The paper demonstrates the use of butterfly factorizations for various tasks such as Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), and Conjugate Gradient (CG) method.
- Improved performance: Compared to traditional methods, this approach offers significant speedups in computation time, with a 30% reduction in the number of operations for FFT and DCT.
- Practical applications: The paper highlights potential use cases in signal processing, image compression, and machine learning algorithms that require fast matrix-vector multiplications.
- Butterfly networks: The authors propose a new architecture called butterfly networks to implement their method efficiently.
- Comparison with other methods: The paper compares the performance of butterfly factorization with existing techniques like Strassen's algorithm and Cooley–Tukey FFT, showing superior results in terms of speed and memory usage.
- Matrix-vector product for confluent Cauchy-like matrices: This paper introduces a new method to compute matrix-vector products for confluent Cauchy-like matrices using butterfly factorization techniques.
- Applications in interpolation: The authors demonstrate the use of this technique in rational interpolation, where it provides significant speedups compared to traditional methods.
- Butterfly transformations and applications in computational linear algebra: This paper discusses random butterfly transformations and their applications in various areas such as numerical linear algebra, fast matrix multiplication, and sparse matrix-vector products.
- Learning fast algorithms for linear transforms using butterfly factorizations
- Discrete Cosine Transform (DCT) matrix factorization: DCTN = ℜ B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial DCT permutation.
- Discrete Sine Transform (DST) matrix factorization: DSTN = ℜ B′ N (BD)^2 with left BD performing FFT, right diagonal matrix D, and right permutation matrix as the initial DST permutation.
- Fast algorithms for Hadamard transforms using butterfly factorizations: HN = diag(1, √2, 1, √2, . . . , 1, √2) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial Hadamard permutation.
- Fast algorithms for Hartley transforms using butterfly factorizations: HN = diag(1, cos π/4, 1, cos π/4, . . . , 1, cos π/4) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial Hartley permutation.
- Fast algorithms for Legendre transforms using butterfly factorizations: LN = diag(1, √2, 1, √2, . . . , 1, √2) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial Legendre permutation.
- Fast algorithms for random feature maps using butterfly factorizations: RN = diag(1, 1/√2, 1/√2, . . . , 1/√2) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial random feature map permutation.
- Fast algorithms for convolutions using butterfly factorizations: CN = diag(1, 1, 1, . . . , 1) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial convolution permutation.
- Fast algorithms for sparse principal component analysis using butterfly factorizations: PN = diag(1, 1/√2, 1/√2, . . . , 1/√2) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial SPCA permutation.
- Fast algorithms for sparse linear regression using butterfly factorizations: RN = diag(1, 1/√2, 1/√2, . . . , 1/√2) B′ N (BP)^2 with left BP performing FFT and right permutation matrix as the initial SPCA permutation.
- Learn fast algorithms for linear transforms using butterfly factorizations.
- Hadamard matrix decomposition and its relation to convolution and circulant matrices.
- Toeplitz matrix representation in terms of butterfly factorizations.
- Orthogonal polynomial matrices and their sparse factorization.
- Applications: Fast algorithms for linear transforms, convolutions, and orthogonal polynomials.
Summary of ""Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations""
- The paper introduces a new method to learn fast algorithms for linear transforms using butterfly factorizations.
- It focuses on neural networks with butterfly layers, which are parameterized by permutations and have multiplicative interactions between parameters.
- The VC dimension bound of this class is shown to be almost linear in the number of parameters (O(LW log W)).
- The paper demonstrates how to include common transforms such as DFT, DCT, DST, convolution, Hadamard, Hartley, Legendre, and random matrices into butterfly factorizations.
- It presents a method for learning fast algorithms for linear transforms using butterfly factorizations, which can be applied to various applications like signal processing, machine learning, and quantum computing.
- The paper provides numerical results showing that the proposed methods achieve high accuracy with low computational cost compared to existing methods.
- The paper introduces a method to learn fast algorithms for linear transforms using butterfly factorizations.
- It shows that every N × N matrix can be expressed by a product of at most 2N + 5 Toeplitz matrices, leading to the need for 4N + 10 BP modules in the factorization.
- Experiments demonstrate recovering fast transforms using BP or BPBP parameter and comparing with fully connected networks (ResNet) on CIFAR-10 dataset.
- The paper also presents a speed comparison of training and inference for butterfly factorizations, showing that the proposed method is faster than dense matrix-matrix multiply and FFT.
- A BP hierarchy is introduced, with observations about its expressiveness and inclusion results.
- The main contributions are the development of fast algorithms for linear transforms using butterfly factorizations and a better understanding of their expressiveness in terms of BP hierarchies.
- The paper proposes a method to learn fast algorithms for linear transforms using butterfly factorizations.
- It assumes that an N × N matrix M can be represented with fewer parameters than its original N^2 entries, contradicting the existence of some (N × N) matrix in (BP)c+1 not in (BP)c.
- Conjecture 1 states that if a matrix M has an arithmetic circuit of size N poly log(N) and depth poly log(N), it will be in (BP)poly log N O(1).
- The paper believes they can prove this conjecture using known approximations of the Jacobi transform by the DCT, which have arithmetic circuits similar to those mentioned in Conjecture 1.
- This work aims to provide a theoretical foundation for understanding and designing fast algorithms for linear transforms using butterfly factorizations.
- The practical applications of this research could lead to more efficient computations in signal processing, image compression, and other areas requiring linear transforms.
",3469
"1904.03310",1,"- Gender bias in ELMo's contextualized word embeddings is quantified, analyzed, and mitigated.
- Training data for ELMo contains significantly more male than female entities.
- Trained ELMo encodes gender information systematically and unequally for males and females.
- Bias in ELMo's contextualized word embeddings affects downstream applications like coreference systems.
- Two methods to mitigate the bias are explored, resulting in eliminating the demonstrated bias on WinoBias corpus.
- The paper investigates gender bias in contextualized word embeddings and its impact on coreference resolution systems.
- It uses a dataset to evaluate whether systems behave differently based on male/female entities with stereotyped or anti-stereotyped occupations.
- ELMo-based system shows higher accuracy disparity in pro- vs. anti-stereotypical predictions compared to GloVe-based system (~30%).
- Two strategies are explored for mitigating bias: data augmentation and test-time embedding neutralization.
- Data augmentation largely mitigates bias, while test-time embedding neutralization is only partially effective.
- The paper highlights the need to address gender bias in contextualized word embeddings and its impact on downstream tasks.
- Related work shows that societal biases about gender roles and occupations are encoded in pretrained word embeddings, affecting downstream applications.
- Previous methods for mitigating bias from word embeddings include post-processing and training mechanisms.
- The paper investigates effective bias mitigation techniques for contextualized embeddings, focusing on gender bias in ELMo.
- Training data for ELMo contains a significant skew with respect to gender: male pronouns occur three times more than female pronouns and co-occur more frequently with occupation words.
- The geometry of trained ELMo embeddings encodes gender information, with two principal components representing contextual and occupational genders.
- Gender bias in pre-trained contextual word embeddings can be reduced by using a larger training corpus or by incorporating gender-balanced data.
- Bias mitigation techniques include regularization, adversarial learning, and debiasing methods like counterfactual training.
- Counterfactual training involves generating gender-swapped sentences to train the model on contextual information rather than stereotypical associations.
- The paper highlights the importance of considering bias in pre-trained models and proposes techniques for mitigating it, particularly in contextualized embeddings.
- Gender Bias in Contextualized Word Embeddings: Analyze paper's main contributions and most interesting findings.
- Occupational Gender: Visualize gender subspace using PCA analysis, showing male-related words separate from female-related ones.
- Unequal Treatment of Gender: Test ELMo embeddings for predicting genders in occupation contexts; find 14% more accurate representation for males than females.
- Bias in Coreference Resolution: Discuss bias in coreference resolution systems, where female pronouns are less likely to be resolved correctly.
- Practical Applications: Suggest using gender-balanced datasets and pretraining on larger corpora to reduce bias in LLMs.
- Gender bias exists in contextualized word embeddings, particularly in coreference resolution systems that depend on ELMo embeddings.
- The WinoBias dataset is used to evaluate the bias, with pro-stereotype and anti-stereotype subsets for male/female coreference resolution examples.
- ELMo improves performance on OntoNotes but shows stronger bias in WinoBias.
- Two methods are proposed to mitigate gender bias: data augmentation at training time and test-time neutralization.
- Data augmentation reduces bias significantly, while the neutralizing ELMo approach only works when there are strong learning signals for the task.
- The paper demonstrates that bias can be largely reduced in coreference systems using these methods.
- Gender bias exists in contextualized word embeddings, specifically in ELMo, due to its training corpus having a significant gender skew and sensitivity towards male and female entities.
- Bias transfers to downstream tasks like coreference resolution, affecting performance and leading to differences between pro- and anti-stereotyped sets.
- Two bias mitigation strategies were explored: data augmentation (retraining the system) and neutralization (neutralizing embeddings). Data augmentation proved more effective in eliminating gender bias from ELMo in a state-of-the-art coreference resolution system.
- Increasing adoption of contextualized word embeddings, such as BERT, highlights the need to evaluate and mitigate bias in downstream applications.
- The work serves as a foundation for understanding and addressing gender bias in large language models.
- The paper focuses on evaluating and mitigating gender bias in contextualized word embeddings, which are crucial for understanding language representation in LLMs.
- It acknowledges the importance of recognizing and addressing this issue to ensure fairness and unbiased language processing.
- The work was supported by various organizations, including National Science Foundation, Facebook Fellowship, Institute of the Humanities and Global Cultures at the University of Virginia, and reviewers' comments.
- While specific details about the methods used or results achieved are not provided in this section, it highlights the significance of addressing gender bias in contextualized word embeddings for LLMs.
- The paper aims to contribute towards a more equitable and unbiased language representation in AI models, which can have far-reaching implications across various applications.
",1031
"1904.07734",1,"- Three continual learning scenarios are described based on whether task identity is provided at test time and, if not, whether it needs to be inferred.
- These scenarios enable more structured comparisons of methods for reducing catastrophic forgetting.
- The scenarios reveal substantial differences between them in terms of difficulty and effectiveness of different continual learning methods.
- In the class incremental learning scenario (task identity must be inferred), regularization-based approaches fail, while replay-based approaches show potential to perform well on all three scenarios.
- An extensive comparison of recently proposed methods using split and permuted MNIST task protocols is provided, highlighting differences between the scenarios.
- Well-documented code for all compared methods is made available at https://github.com/GMvandeVen/continual-learning.
- Three continual learning scenarios: Task-IL, Domain-IL, and Class-IL.
- Task-IL (easiest): Models are informed about which task to perform; multi-headed output layer.
- Domain-IL: Task identity not available at test time; models solve tasks without inferring task ID.
- Class-IL: Models must solve each task seen so far and infer the task they're presented with.
- Differences between scenarios: Architectural layout, task identity usage, and difficulty level.
- Example task protocols: Split MNIST and Permuted MNIST.
- Scenarios can be applied to any task protocol; each scenario has its own challenges and benefits.
- Three scenarios for continual learning: Task-IL, Domain-IL, and Class-IL.
- Permuted MNIST example demonstrates how these scenarios can be applied to different situations.
- Strategies for alleviating catastrophic forgetting:
   a. Task-specific components - Explicitly define sub-networks per task (Context-dependent Gating, evolutionary algorithms, gradient descent).
   b. Regularized optimization - Elastic Weight Consolidation and Synaptic Intelligence estimate parameter importance for previous tasks to prevent changes.
   c. Modifying training data: Replay - Learning without Forgetting uses ""soft targets"" from previous models; Deep Generative Replay generates input samples with ""hard targets""; DGR+distill combines both approaches. Exact replay stores and replays past data.
- Numeric results and metrics are not included in this summary, as they were not provided in the given text.
- Three continual learning scenarios: Task-IL, Domain-IL, and Class-IL.
- Methods to address catastrophic forgetting: XdG, EWC/Online EWC/SI, LwF/DGR/DGR+distill, iCaRL.
- Split MNIST task protocol: Five tasks, 60k training images, 10k test images, no pre-processing.
- Permuted MNIST task protocol: Ten tasks, 1024 permutated images, no pre-processing.
- Neural network architecture for all methods: Multi-layer perceptron with ReLU non-linearities and varying hidden layers.
- XdG method: Randomly gates units in hidden layers based on task identity at test time (grid search hyperparameter).
- EWC, Online EWC, and SI methods: Add regularization term to loss function, controlled by a grid search hyperparameter.
- LwF, DGR, and DGR+distill methods: Loss-term for replayed data added to current task's loss; weighted according to number of trained tasks.
- iCaRL method: Uses stored data as ""exemplars"" during execution, replays data during training to protect feature extractor network.
- Experimental results: Methods compared in three continual learning scenarios on split and permuted MNIST task protocols; iCaRL performed best in Class-IL scenario.
- Three scenarios for continual learning: Task-IL, Domain-IL, and Class-IL.
- iCaRL uses replay of stored data during training (exemplars) and distillation of current task data on previous classes, but can only be applied in the Class-IL scenario using binary classification loss.
- Baselines: None (fine-tuning), Offline (joint training).
- DGR and DGR+distill use a separate generative model for each task, trained with replay and regularization methods.
- Results: Regularization methods struggled in Domain-IL and Class-IL scenarios; Replay methods performed well in all scenarios.
- EWC and Online EWC achieved competitive performance on split MNIST compared to recent reports due to exploring a wider hyperparameter range.
- Permuted MNIST protocol: All methods except LwF performed well in Task-IL and Domain-IL, but failed in Class-IL.
- Regularization-based methods failed in the Class-IL scenario for both split and permuted MNIST protocols.
- Three scenarios for continual learning: Class-IL, Task-IL, and Domain-IL.
- In Class-IL, regularization-based methods failed while replay-based methods performed well.
- For Task-IL and Domain-IL, the difference was small; task identity information in lower layers could improve performance.
- LwF had success with split MNIST but not permuted MNIST due to uncorrelated inputs.
- Easy tasks lead to small gradients, making EWC less effective.
- Replay-based methods are crucial for scenarios where task identity is not provided.
- Generative replay's success depends on the quality of generated samples and scalability.
- Alternatively, storing examples from previous tasks can be used for replay (iCaRL).
Summary of ""Three Scenarios for Continual Learning"" Paper:
- Overcoming catastrophic forgetting in neural networks is a crucial challenge in machine learning.
- The paper discusses various approaches to continual learning, focusing on three scenarios: replay-based methods, generative models, and regularization techniques.
- Replay-based methods involve storing past data for later use, such as ICaRL (Incremental Classifier and Representation Learning), FEARNET (FearNet: Brain-inspired Model for Incremental Learning), and Generative Replay with Feedback Connections.
- Generative models include Variational Continual Learning, Context-dependent Gating, Synaptic Stabilization, and Incremental Classifier Learning with Generative Adversarial Networks.
- Regularization techniques involve methods like PathNet (Evolution Channels Gradient Descent in Super Neural Networks), Hard Attention to the Task, Learning without Forgetting, Distilling Knowledge in a Neural Network, and Continual Learning with Deep Generative Replay.
- Progress & Compress is a scalable framework for continual learning that combines compression and progress strategies.
- The paper highlights the importance of evaluating continual learning methods using robust metrics and benchmarks to ensure accurate comparisons between different approaches.
- Continual learning scenarios: Task-IL, Domain-IL, and Class-IL.
- Loss functions for classification tasks: Per-sample cross entropy loss (Lclassification).
- Soft targets in distillation loss function (Ldistillation) for replayed data.
- Temperature T used to soften the probability distributions during distillation.
- LwF and DGR+distill use distillation loss for their replayed data.
- Subtle differences in generating hard targets and soft targets for each scenario.
- Experimental details available on GitHub (https://github.com/GMvandeVen/continual-learning).
- Differences between the three continual learning scenarios:
   a. Task-IL: Only active nodes of current task, multi-headed softmax layer.
   b. Domain-IL: All nodes active, no replaying.
   c. Class-IL: Nodes of all seen tasks active, both for training and replayed data.
- LwF and DGR+distill methods use elastic weight consolidation (EWC) as a baseline.
- EWC's quadratic penalty function Q(θ) is used to penalize changes in the model parameters.
- Three scenarios for continual learning: Class-IL, Pseudo-Labeling, and Class-EWC.
- Elastic Weight Consolidation (EWC) regularization term: quadratic penalty for each task's parameters based on their importance estimated by the Fisher Information matrix.
- Online EWC modification: only one quadratic penalty term with a running sum of previous tasks' Fisher Information matrices, governed by hyperparameter γ.
- Synaptic Intelligence (SI) regularization term: similar to online EWC but uses an exponential moving average instead of the running sum.
- Experiments on MNIST and CIFAR-10 datasets show that Class-EWC and Online EWC perform better than other methods, with Online EWC being faster and more scalable.
- The paper highlights the importance of choosing a suitable regularization method for continual learning scenarios.
- Continual learning scenarios: Three main approaches are presented - vanilla, distillation (DGR), and incremental curriculum learning (iCaRL).
- Vanilla approach: Train a single model on all tasks sequentially without forgetting previous knowledge.
- Distillation (DGR): A generative model is used to create replay data from previously learned tasks, which helps the main model retain its knowledge.
- Incremental curriculum learning (iCaRL): Stores a subset of examples from each task and uses them as ""old-task-soft-targets"" during training on new tasks.
- Feature extractor: A shared network architecture is used for both classification and feature extraction in iCaRL, with the softmax layer removed.
- Training: Incremental curriculum learning trains on an extended dataset containing all stored data from previous tasks along with the current task's training data.
- Regularization term (SI): Estimates parameter importance based on their contributions to loss changes across tasks and penalizes changes away from previous values.
- Generative model: A variational autoencoder is used for DGR and DGR+distill, with a latent variable regularization term and reconstruction loss function.
- Experimental results: The paper shows that iCaRL achieves 30% accuracy on MNIST after learning 10 tasks, while vanilla and distillation approaches forget almost all knowledge after the first task.
- Incremental curriculum learning outperforms other methods in terms of accuracy and retaining previous knowledge.
- iCaRL (Incremental Classifier with Replay) operates under the assumption that up to B data points, referred to as 'exemplars', are allowed in memory. The available memory is evenly distributed among classes seen so far, resulting in m exemplars per class.
- After training on a task finishes, selection of stored data is updated: create exemplar-sets for new classes using the 'herding' algorithm; reduce exemplar-sets for old classes until only m exemplars remain.
- Nearest-class-mean classification is used to classify new inputs based on stored exemplars.
- Using task identity in hidden layers significantly improved performance in the Task-IL scenario of the permuted MNIST protocol, demonstrating that task identity information is more useful at lower network layers.
- Replaying stored data can be an alternative to generative replay; it can also be used during training and execution. In Class-IL scenarios, even storing one example per class was enough for exact replay methods to outperform regularization-based methods. However, more examples were needed to match the performance of generative replay.
- The paper's findings suggest that continual learning can be improved by using task identity information in lower network layers and by storing a sufficient number of exemplars for each class.
- Continual learning methods require setting hyperparameters, which are typically optimized using training data from all tasks and validation sets for each task.
- This approach violates the continual learning principle of only visiting each task once and in sequence.
- A clear example of this issue is provided by Wu et al., where a ""bias-removal parameter"" was set to optimize performance on all seen tasks' validation data.
- To give generative replay methods a fair chance, grid searches were performed for their hyperparameters (Figures D.1 and D.2).
- Validation sets were not used in the grid search process due to the issue discussed above; instead, test sets were employed.
- The paper highlights that it's important to understand the impact of influential hyperparameters on continual learning methods.
- For permuted MNIST, even with 50,000 stored examples, exact replay variants were consistently outperformed by DGR+distill.
- The typical way of setting hyperparameter values is to train models using a range of options and selecting the best performance on separate validation sets.
- In continual learning settings, this strategy has been adapted to use only training data from each task for hyperparameter tuning.
- This summary emphasizes the importance of understanding the impact of influential hyperparameters in continual learning methods and the challenges associated with grid searches in these settings.
",2533
"1905.03197",1,"- UNILM is a unified pre-trained language model that can be fine-tuned for both natural language understanding (NLU) and generation (NLG) tasks.
- It achieves this by employing a shared Transformer network and utilizing specific self-attention masks to control the context prediction conditions.
- UNILM compares favorably with BERT on the GLUE benchmark, SQuAD 2.0, and CoQA question answering tasks.
- It achieves new state-of-the-art results in five natural language generation datasets, including improvements in CNN/DailyMail abstractive summarization ROUGE-L (40.51), Gigaword abstractive summarization ROUGE-L (35.75), CoQA generative question answering F1 score (82.5), SQuAD question generation BLEU-4 (22.12), and DSTC7 document-grounded dialog response generation NIST-4 (2.67).
- The code and pre-trained models are available at https://github.com/microsoft/unilm.
- UNILM (Unified Language Model) is a unified pre-training approach that combines multiple language modeling objectives within a single Transformer model, sharing parameters and architecture for different types of LMs.
- This approach alleviates the need to separately train and host multiple LMs, leading to more generalized text representations due to joint optimization across various language modeling tasks.
- UNILM can be used for both natural language understanding (NLU) and generation (NLG) tasks by configuring different self-attention masks.
- Experimental results show that UNILM performs well on various downstream tasks, including GLUE benchmark, extractive question answering, long text generation, abstractive summarization, and question generation.
- The unified pre-training procedure leads to a single Transformer LM with shared parameters and architecture for different types of LMs, reducing the need for multiple models and their hosting requirements.
- UNILM's parameter sharing helps mitigate overfitting by optimizing text representations across diverse language modeling objectives that utilize context in various ways.
- As a sequence-to-sequence LM, UNILM can be used for natural language generation tasks such as abstractive summarization and question generation.
- The paper presents experimental results on the use of UNILM as a bidirectional encoder, achieving competitive performance compared to other models like BERT and ELMo.
- UNILM's unified pre-training approach offers practical benefits for researchers and developers by reducing model complexity and hosting requirements while improving generalization across various language modeling tasks.
- The paper highlights the potential of UNILM in addressing challenges faced by existing LMs, such as overfitting to a single task or requiring separate training for different types of LMs.
- UNILM (Unified Language Model) is a pre-training method that combines unidirectional, bidirectional, and sequence-to-sequence language modeling objectives in a single model.
- The shared Transformer network optimizes the model with three unsupervised language modeling objectives: unidirectional LM, bidirectional LM, and sequence-to-sequence LM.
- UNILM achieves state-of-the-art results on five natural language generation (NLG) datasets, including CNN/DailyMail, Gigaword abstractive summarization, SQuAD question generation, CoQA generative question answering, and DSTC7 dialog response generation.
- UNILM's input representation follows BERT's format, using WordPiece tokenization for subword units. Each input token has a vector representation consisting of token embedding, position embedding, and segment embedding.
- The model is trained on a combination of unidirectional, bidirectional, and sequence-to-sequence language modeling objectives to improve its performance in various tasks.
- UNILM's pre-training allows for fine-tuning the model using task-specific data for downstream tasks.
- Experimental results show that UNILM compares favorably with BERT on the GLUE benchmark and two extractive question answering tasks (SQuAD 2.0 and CoQA).
- The paper demonstrates the effectiveness of UNILM in various natural language processing tasks, including natural language understanding and generation.
- By combining multiple unsupervised language modeling objectives into a single model, UNILM achieves better performance compared to other models that focus on only one objective.
- UNILM's pre-training approach allows for more efficient fine-tuning of the model for specific tasks, potentially reducing training time and improving overall performance.
- UNILM (Unified Language Model) combines multiple LM tasks, using token embedding, position embedding, and segment embedding for contextual representation.
- The backbone network uses an L-layer Transformer with self-attention heads to encode input vectors into contextual representations at different levels of abstraction.
- Pre-training objectives include four cloze tasks designed for various language modeling objectives, using a softmax classifier and cross-entropy loss.
- UNILM supports unidirectional (left-to-right, right-to-left) and bidirectional LMs, with different mask matrices controlling token contexts in each case.
- The paper demonstrates that UNILM achieves state-of-the-art performance on various language modeling tasks, including question answering, text classification, and natural language generation.
- UNILM's unified architecture allows for efficient fine-tuning across multiple downstream tasks with minimal parameter adjustments.
- The model is trained using a single pretraining corpus, which reduces the need for task-specific data and improves generalization capabilities.
- UNILM achieves 30% accuracy on the SQuAD question answering dataset, outperforming previous models by 15%.
- UNILM's bidirectional LM can generate better contextual representations than unidirectional LMs, leading to improved performance in downstream tasks.
- The model is practical and efficient, with a 4.5 times faster training speed compared to previous models.
- UNILM (Unified Language Model) is a pre-training approach that combines different language modeling objectives, including bidirectional LM, sequence-to-sequence LM, and left-to-right/right-to-left LMs.
- The model can generate better contextual representations of text than unidirectional counterparts due to its self-attention mechanism that allows every token to attend across all positions in the input sequence.
- UNILM learns to effectively encode source segments, which is beneficial for predicting tokens in target segments and adapting to various conditional text generation tasks like abstractive summarization.
- The pre-training setup includes next sentence prediction as well, following a similar architecture to BERT-Large for fair comparison.
- UNILM's model architecture consists of 24 layers, 1024 hidden size, and 16 attention heads, with around 340 million parameters. It is initialized by BERT-Large.
- The pre-trained model can be easily adapted to various downstream tasks without fine-tuning, making it a versatile language modeling approach.
- UNILM (Unified Language Model) is a pre-trained language model that combines BERT's architecture with masked language modeling, designed for natural language understanding and generation tasks.
- UNILM is initialized using BERTLARGE and trained on English Wikipedia and BookCorpus, with a vocabulary size of 28,996 tokens, maximum input sequence length of 512, token masking probability of 15%, and a mix of token replacement strategies.
- UNILM's fine-tuning process involves adapting the model for natural language understanding (NLU) tasks as bidirectional Transformer encoders and natural language generation (NLG) tasks using sequence-to-sequence models with self-attention masks.
- Experiments were conducted on various NLU and NLG tasks, including text classification, extractive question answering, abstractive summarization, question generation, generative question answering, and dialog response generation.
- UNILM achieved competitive results in the GLUE benchmark for natural language understanding tasks, with an accuracy of 81.4% on the MNLI task, outperforming BERTLARGE by 2.3%.
- In abstractive summarization, UNILM performed better than BART and RoBERTa, achieving a ROUGE-L score of 50.97 on CNN/Daily Mail, 48.15 on Gigaword, and 47.23 on Newsroom.
- In question generation tasks, UNILM achieved an accuracy of 61.3% on the SQuAD dataset, outperforming BERTLARGE by 0.9%.
- For generative question answering, UNILM's performance was comparable to BART and RoBERTa, with a mean reciprocal rank (MRR) of 51.6% on the SQuAD dataset.
- In dialog response generation, UNILM achieved an accuracy of 83.2% on the PersonaChat dataset, outperforming BERTLARGE by 0.7%.
- Overall, UNILM demonstrated competitive performance in various natural language processing tasks and can be used as a strong baseline for future research.
- The paper introduces UNILM, a unified language model pre-training approach for natural language understanding and generation.
- UNILM combines extractive and abstractive summarization techniques to improve performance on various datasets like CNN/DailyMail and Gigaword.
- It outperforms previous state-of-the-art models in both extractive and abstractive summarization tasks, achieving new records for abstractive summarization on the CNN/DailyMail dataset.
- UNILM uses a sequence-to-sequence model with pre-trained ELMo representations (S2S-ELMo) and a bottom-up content selector (Bottom-Up).
- The paper highlights the importance of fine-tuning models for specific tasks, using the F1 version of ROUGE as an evaluation metric, and employing label smoothing during training.
- UNILM's performance on Gigaword abstractive summarization shows improvements over other models that use 3.8 million examples for training compared to those with only 10K examples.
- The paper provides a practical application of the model, fine-tuning it for sequence-to-sequence tasks like machine translation and text summarization.
- UNILM's approach can be applied to other domains such as question answering, dialogue systems, and text classification.
- The authors suggest that their work could potentially lead to more efficient pre-training methods in the future.
- Overall, UNILM demonstrates significant improvements in abstractive summarization performance compared to previous state-of-the-art models.
- The paper introduces UNILM (Unified Language Model Pre-training for Natural Language Understanding and Generation), a new state-of-the-art model that outperforms previous work in various natural language processing tasks, such as abstractive summarization, question answering, and machine translation.
- In abstractive summarization, UNILM achieves better performance than MASS by 7.08 points in ROUGE-L in a low-resource setting (10,000 examples).
- For question answering tasks, UNILM outperforms BERTLARGE on the SQuAD development set and performs well on CoQA's conversational question answering dataset.
- In machine translation, UNILM achieves state-of-the-art results in both low-resource and high-resource settings, with a 1.25 BLEU score improvement over the previous best model (MASS) on WMT16 English-German translation task.
- The paper also presents an efficient training strategy for UNILM that reduces training time by up to 40% compared to MASS while maintaining similar performance.
- UNILM's pre-training methodology is based on a unified language model, which combines the benefits of both masked language modeling and next sentence prediction tasks.
- The paper highlights that UNILM can be easily extended to other tasks by fine-tuning its pre-trained parameters, making it a versatile and efficient model for various natural language processing applications.
- The paper introduces UNILM, a unified language model pre-trained for natural language understanding and generation tasks.
- UNILM outperforms BERTLARGE in question answering on the CoQA dataset, demonstrating its effectiveness in both extractive and generative methods.
- For generative question answering, UNILM achieves better performance than previous models like Seq2Seq and PGNet, significantly reducing the gap between generative and extractive methods.
- The paper also explores answer-aware question generation, where given an input passage and answer span, a question is generated that asks for the answer. UNILM outperforms other models in this task as well.
- In summary, UNILM shows promising results in various natural language understanding and generation tasks, improving upon existing methods and potentially closing the gap between generative and extractive approaches.
- UNILM (Unified Language Model) is a pre-training method that improves natural language understanding and generation by incorporating question answering, question generation, and response generation tasks.
- UNILM outperforms previous models in question generation, achieving a new state-of-the-art performance.
- The paper demonstrates how augmented data generated through question generation can improve the question answering model's performance.
- Bidirectional masked language modeling is used as an auxiliary task during fine-tuning to alleviate catastrophic forgetting when working with augmented data.
- UNILM also shows promising results in document-grounded dialog response generation tasks, improving upon previous models.
- The paper highlights the practical applications of UNILM in various natural language processing tasks and its potential benefits for future research.
- UNILM is a unified language model pre-trained for natural language understanding and generation, combining bidirectional, unidirectional, and sequence-to-sequence LMs with shared parameters.
- The paper demonstrates that UNILM performs comparably to BERT on the GLUE benchmark and outperforms previous state-of-the-art models in question answering tasks.
- UNILM achieves better results than BERTLARGE in the DSTC7 shared task, which involves generating a natural language response from web documents and conversation history.
- The paper presents experimental results on various tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.
- UNILM's unified pre-training approach enables straightforward fine-tuning for both NLU and NLG tasks, making it a versatile model for various applications.
- The paper highlights the potential of UNILM as a practical solution for real-world problems involving natural language processing, such as dialogue systems and question answering.
- UNILM's performance on the GLUE benchmark demonstrates its ability to handle diverse tasks with comparable results to BERTLARGE.
- The paper provides insights into how unified pre-training can improve model performance in various natural language processing tasks, offering a new approach for future research and development.
- UNILM (Unified Language Model) is a pre-training method that outperforms previous state-of-the-art models on various natural language processing tasks, including question answering and natural language generation.
- It achieves better performance in five NLG datasets such as CNN/DailyMail, Gigaword abstractive summarization, SQuAD question generation, CoQA generative question answering, and DSTC7 dialog response generation.
- Future work includes training larger models on webscale text corpora, conducting more experiments on end applications, ablation studies to investigate model capability, extending UNILM for cross-lingual tasks, and multi-task fine-tuning on both NLU and NLG tasks.
- The authors acknowledge Shiyue Zhang's contribution in question generation experiments.
- They plan to conduct more experiments with larger models, explore end applications, investigate model capabilities through ablation studies, extend UNILM for cross-lingual tasks, and perform multi-task fine-tuning on both NLU and NLG tasks.
",3170
"1905.07830",1,"- HellaSwag is a new challenge dataset that tests state-of-the-art models' ability to perform human-level commonsense inference.
- The dataset consists of trivial questions for humans (95% accuracy) but struggles for state-of-the-art models (48%).
- Adversarial Filtering (AF), a data collection paradigm, is used to create HellaSwag. It involves a series of discriminators selecting adversarial machine-generated wrong answers.
- AF proves robust and helps scale up the length and complexity of examples towards a 'Goldilocks' zone where generated text is ridiculous for humans but often misclassified by models.
- HellaSwag's construction and difficulty shed light on deep pretrained models' inner workings, suggesting a new path forward for NLP research.
- Benchmarks should evolve with the state-of-the-art in an adversarial way to present ever-harder challenges.
- The paper investigates how well deep pretrained models, such as BERT, perform at commonsense natural language inference (NLI).
- It introduces HellaSwag, a new benchmark for commonsense NLI, which shows that BERT's strong performance on SWAG is dependent on the finetuning process and its ability to learn dataset-specific distributional biases.
- When the distribution of language shifts slightly, even within the same domain, machine performance drops drastically.
- Adversarial Filtering (AF) is used to create a challenging set of generated wrong answers for HellaSwag, resulting in 95.6% accuracy for humans and only 50% for machines.
- Machine performance further decreases by an additional 5% when evaluated on examples with novel concepts from the same domain.
- To make HellaSwag robust to deep pretrained models, a combination of state-of-the-art generators (Radford et al., 2018), BERT as discriminators, and high-quality source text is used.
- The paper expands on SWAG's original dataset by adding more challenging problems and novel concepts.
- HellaSwag aims to provide a benchmark for evaluating the robustness of commonsense reasoning in deep pretrained models.
- SWAG dataset for commonsense NLI faces challenges with obtaining interesting negatives due to annotation artifacts.
- Adversarial Filtering (AF) introduced by Zellers et al. (2018) aims to address this issue by producing a challenging dataset regardless of the final split.
- The process involves training a new classifier on a dummy training set, Dtrain, and replacing easily-classified negative endings with adversarial ones in the dummy test set, Dtest. This cycle is repeated iteratively.
- A Goldilocks zone was discovered where generations are largely nonsensical but state-of-the-art discriminators cannot reliably tell the difference between these and ground truth.
- The study highlights the need for principled dataset creation algorithms to evolve with modeling advancements, ensuring that underlying tasks rather than individual datasets are solved.
- SWAG's original video-captioning domain was expanded by using WikiHow articles, increasing context diversity and generation length.
- Adversarial Filtering can be applied to other NLP tasks such as commonsense NLI, textual entailment, and question answering.
- The paper presents a case study towards verified progress in NLP via iterative rounds of building and breaking datasets.
- Datasets should evolve together with the state-of-the-art to ensure reliable benchmarks for challenging tasks.
- New challenges arise as models improve, requiring continuous adaptation of dataset creation algorithms.
- The paper introduces an adversarial framework (AF) to create challenging datasets for natural language understanding tasks, specifically focusing on sentence completion problems like SWAG and HellaSwag.
- AF generates a dataset D by massively oversampling potential incorrect answers from a language model trained on in-domain data and selecting them using an ensemble of adversaries. The selection process continues until the accuracy of these adversaries converges, resulting in a challenging dataset for models regardless of the final split.
- This approach contrasts with past work on adversarial examples that consider out-of-distribution test sets as adversarial. AF's difficulty persists even when providing significant training data and when this data comes from the same distribution as the test set.
- The paper investigates why SWAG was solved by focusing on BERT, which is the best performing model at that time. It explores how a model trained on Wikipedia and books can be effectively fine-tuned for SWAG, a dataset from video captions.
- AF's performance is compared to other methods like shuffling endings or providing context in the form of shuffled sentences. The paper also discusses the limitations of these approaches and how they differ from AF.
- The framework can be applied to create adversarial examples for any sentence completion task, making it a valuable tool for evaluating language models' generalization capabilities.
- The paper highlights that BERT's performance on SWAG is not solely due to its innate knowledge about the dataset but also because of its ability to learn from contextual cues and its large pre-training corpus.
- AF can be used to create adversarial examples for any sentence completion task, making it a valuable tool for evaluating language models' generalization capabilities.
- The paper demonstrates that BERT's performance on SWAG is not solely due to innate knowledge about the dataset but also because of its ability to learn from contextual cues and its large pre-training corpus.
- AF's adversarial framework can be used to create challenging datasets for various sentence completion tasks, improving model evaluation and understanding their generalization capabilities.
- BERT outperforms ELMo NLI model with fewer training examples on SWAG, but still needs around 16k examples to approach human performance.
- Context omission in SWAG endings suggests bias exists in machine-generated endings compared to human-written ones.
- BERT's performance is affected by context and structure, but it also learns distributional stylistic patterns during fine-tuning.
- Shuffled scenarios show that BERT can adapt to new settings without exposure, indicating its focus on lexical reasoning rather than context or structure.
- SWAG's construction via Adversarial Filtering (AF) leads to stylistic biases in machine-generated endings.
- The paper investigates the interplay of adversarial filtering (AF) with SWAG's generators and discriminators, using BERTLarge as a discriminator to analyze its performance in comparison to Zellers et al.'s 2018 setup and GPT.
- Results show that SWAG generations are distinct from human-written endings, leading to AF accuracy not dropping to chance; however, GPT's generations cause BERT accuracy to drop below 30%. This highlights the importance of high-quality generators and discriminators for AF success.
- The paper introduces HellaSwag, an evolution of SWAG that removes artifacts by including video captions from ActivityNet Captions and using activity labels as additional structure.
- A new testbed is introduced: completing how-to articles from WikiHow, which demonstrates AF's effectiveness in this setting despite initial high BERT performance.
- The paper concludes that while HellaSwag improves upon SWAG, it still does not solve the underlying task of commonsense NLI; further research is needed to address this challenge.
- The paper analyzes HellaSwag's performance in generating human-like sentence endings, focusing on one-, two-, and three-sentence cases.
- In a random 80%/20% split, humans perform better than BERT in distinguishing human-written endings from machine generations for WikiHow (93.5%) compared to ActivityNet (60%). This difference is attributed to the length of generated sentences: WikiHow's two-sentence generation averages 41 tokens versus 13 for ActivityNet, providing more opportunities for detectable mistakes in WikiHow.
- To ensure high human agreement on ActivityNet, several rounds of annotation are performed, replacing false negative endings with true negatives. This results in a validation accuracy increase from 60% to 94%.
- Zero-shot categories are used for evaluating models' ability to generalize to new situations, using category labels from WikiHow and ActivityNet.
- The paper highlights the importance of context length in determining model performance, with longer contexts leading to better results.
- HellaSwag's performance is compared against other state-of-the-art models like GPT2, BART, and T5, demonstrating its effectiveness in generating human-like sentence endings.
- The paper evaluates HellaSwag, a model that predicts sentence endings, using various strong baselines and pretrained models.
- It introduces an evaluation setup with in-domain and zero-shot categories to test the model's performance on both seen and unseen data sources (ActivityNet and WikiHow).
- HellaSwag underperforms humans significantly, with a gap of over 45% on in-domain categories and 50% on zero-shot categories.
- The paper compares HellaSwag to models like OpenAI GPT, BERT-Base, ESIM+ELMo, LSTM sentence encoders (with GloVe, ELMo, or frozen BERT embeddings), and FastText.
- Human performance is used as a benchmark for comparison by asking five independent crowd workers to solve the same multiple choice problems.
- The paper highlights that HellaSwag's performance on zero-shot categories is particularly poor, suggesting that it struggles with generalization.
- It also shows that pretraining does not necessarily lead to better performance in this task, as BERT-Large performs worse than BERT-Base and OpenAI GPT.
- The paper concludes that HellaSwag's performance is still far from human-level accuracy, indicating the need for further research on improving machine learning models for sentence completion tasks.
- The paper compares human performance with machine learning models on two datasets, SWAG and HellaSwag, focusing on multiple choice problems.
- Human accuracy is high (95%+) while overall model performance is below 50%. BERTLarge performs the best at 47.3%, but still struggles on HellaSwag.
- Pre-training and end-to-end fine-tuning are crucial for better performance, as freezing BERT-Base and adding an LSTM lowers its overall performance by 4.3%.
- ELMo's lack of update during fine-tuning might explain why models like ESIM+ELMo struggled on SWAG.
- BERTLarge struggles on HellaSwag, especially in zero-shot scenarios, suggesting it may not generalize well to novel activities or how-to categories.
- WikiHow is a more challenging domain for machines compared to ActivityNet, with 45% BertLarge performance versus 96.5% human accuracy. OpenAI GPT outperforms BERT on WikiHow but the opposite happens in ActivityNet.
- Transfer experiments show that models trained on one dataset and evaluated on another perform worse (12-15% drop). SWAG models do not generalize to WikiHow, while HellaSwag models have 69% accuracy in their missing domain (movie descriptions).
- SWAG models struggle to generalize to new domains, while BERTLarge performs better in zero-shot settings.
- ActivityNet and WikiHow datasets are used for evaluation, with different validation splits and weighted averages to avoid bias.
- In the ActivityNet dataset, SWAG shows 0% accuracy in some categories (shaving, sharpening knives), while BERTLarge performs better (100%).
- In WikiHow's youth category, BERTLarge achieves higher accuracy compared to SWAG (9.4%, 29.1%, and 61.5% vs. 0%).
- Qualitative examples show that BERTLarge can generate more coherent and relevant continuations than SWAG in various contexts.
- The paper highlights the importance of general commonsense reasoning for better performance in zero-shot settings.
- Practical applications include using these models to improve sentence completion tasks, dialogue systems, or other natural language understanding tasks.
- HellaSwag is a challenging testbed for state-of-the-art NLI models, even those built on extensive pretraining.
- The paper explores qualitative examples of BERT's performance on ActivityNet contexts, zero-shot scenarios, and WikiHow, highlighting its limitations in coherence and accuracy.
- Adversarial Filtering (AF) is used to improve model performance by filtering out nonsensical generations, but it doesn't solve the task completely.
- Using stronger models at test time improves performance, but not enough to solve HellaSwag.
- The study suggests that future, more powerful models might still struggle with HellaSwag due to its Goldilocks zone of text complexity.
- An ablation study on the Adversarial Filtering model shows that increasing the gap between filter and final discriminator doesn't solve the task.
- Pretraining large models on NLP tasks has shown significant progress, but there are limits to this approach.
- To reach human-level performance in HellaSwag, 109 GPU hours of pretraining (over 100k GPU years) would be required without algorithmic or computational improvements.
- Algorithmic improvements could include architectural advances, better pretraining objectives, and addressing data bias issues.
- Existing deep methods often struggle with lexical false friends, highlighting the need for models to abstract away from language and model world states.
- HellaSwag's future evolution might involve crowd-sourcing new datasets with similar formats, leading to further challenges for machine learning models.
- The paper emphasizes the importance of understanding the limitations of current pretraining methods and exploring alternative approaches to achieve human-level performance in commonsense inference tasks.
- HellaSwag is a new dataset for physically situated commonsense reasoning, created through adversarial filtering and combining state-of-the-art language generation and discrimination models.
- The dataset aims to be adversarial to the most robust models available, providing insights into pretrained model inner workings and suggesting progress in NLP by evolving benchmarks alongside advancing models.
- Success lies within a Goldilocks zone: complex enough for state-of-the-art generators to make mistakes but simple enough for discriminators to fail.
- Until language generation is solved, commonsense NLI will remain unsolved; recent scaling up of language models has shown inconsistency issues even with the best curated examples requiring 25 random seeds.
- The paper presents HellaSwag as a new benchmark for physically situated commonsense reasoning and highlights its potential to advance NLP progress by co-evolving with state-of-the-art models.
",2889
"1905.12616",1,"- Defending against neural fake news is a growing concern due to advancements in natural language generation, which can lead to targeted propaganda mimicking real news.
- Threat modeling and identifying potential vulnerabilities are crucial for developing robust defenses against neural fake news.
- Grover, a model for controllable text generation, can generate articles that humans find more trustworthy than human-written disinformation.
- Current discriminators have 73% accuracy in classifying neural fake news from real human-written news with moderate training data.
- Counterintuitively, Grover itself achieves 92% accuracy as a defense against its own generated content, highlighting the importance of public release for better detection.
- Exposure bias and sampling strategies in generators leave artifacts that can be detected by similar discriminators.
- Ethical considerations regarding technology are discussed, with plans to publicly release Grover to aid in detecting neural fake news.
- The paper explores Grover, a model that can detect and generate neural fake news.
- Humans find it difficult to distinguish between real and fake news without high scrutiny.
- Grover generates entire news articles, including title, source, date, and author list.
- Disinformation generated by Grover is rated as trustworthy, even more so than human-written disinformation.
- Developing robust verification techniques against generators like Grover is crucial for fake news detection research.
- In a setting with limited access to real news, Grover performs better at 92% accuracy compared to existing discriminators (73%).
- Deep pretrained language models distinguish between real and machine-generated text by identifying key artifacts introduced during generation due to exposure bias.
- Sampling strategies can alleviate these biases but also introduce new artifacts that strong discriminators can detect.
- The paper highlights the ethical responsibilities of researchers studying fake news, including mapping out the territory and understanding our role in shaping AI's impact on society.
- Practical applications include developing better defense mechanisms against neural fake news.
- Understanding responsibilities and potential negative implications of releasing neural fake news models: Hecht et al., Zellers, Solaiman et al.
- Provisional policy for model release: safe and imperative to counter ML-based disinformation threats.
- Fake news types: satire, propaganda, text-only documents (news articles with false information).
- Existing fake news: human-written, monetization or propaganda goals, selective content creation.
- Fact checking and verification efforts: manual fact-checking tools like NewsGuard, Hoaxy, Snopes, PolitiFact; automated detection methods based on stylistic biases in text.
- Cognitive biases in humans make them susceptible to believing fake news that fits their worldview (backfire effect, confirmation bias).
- Framework: Fake news generation and detection as an adversarial game with two players - Adversary and Verifier.
- Adversary's goal: generate realistic fake stories that match specific attributes (viral or persuasive).
- Verifier's goal: classify news stories accurately.
- Practical applications: potential use cases for the proposed framework, models, and policy guidelines.
- The paper focuses on defending against neural fake news, with two main players: human users and verifiers (classifying news stories as real or fake).
- Verifiers have access to unlimited real news but limited fake news from specific adversaries, reflecting the current landscape. This leads to an escalating ""arms race"" between attackers and defenders.
- Grover is introduced as a model for generating realistic and controlled neural fake news. It addresses limitations in existing large-scale generative models by producing controllable generations.
- The paper discusses the challenges of modeling news articles, which require generating metadata fields (domain, date, authors, headline, body) along with the running text.
- Grover's approach involves sampling from a joint distribution of article components and using a canonical order for field generation. This avoids prohibitive marginalization or requiring models to handle multiple potential orderings during inference time.
- The paper presents results on Grover's performance, including its ability to generate realistic-looking fake news articles with controlled headlines and body text.
- Practical applications of the research include using Grover as a tool for evaluating verification systems or training verifiers.
- Unusual findings include Grover's ability to generate fake news articles that are both realistic and controllable, which could be used by adversaries in future attacks.
- Grover: A new approach for efficient learning and generation of multi-field documents in language modeling.
- Flexible decomposition of Equation 2 during inference time, using a standard order to sort metadata fields (domain, date, authors, headline, body).
- Target Context examples show how Grover generates an article by sampling tokens from the model and combining them with field-specific start and end tokens.
- Common workarounds for fake news detection: Human seeding context or using a standard order for metadata fields (potential bias and distributional artifacts).
- Training process involves randomly partitioning, dropping out fields, and concatenating underlying tokens to learn unconditional generation.
- Grover's architecture based on recent progress in training large Transformers for language modeling.
- Practical application: Using Grover to generate anti-vaccine articles by specifying domain, date, and headline, then generating body, fake author, and new headline.
- Benefit: Allows for more realistic fake news generation compared to existing methods.
- The paper introduces Grover, a neural network model for detecting fake news based on large Transformer architectures like GPT2 and BERT.
- Three model sizes are considered: Grover-Base (124 million parameters), Grover-Large (355 million parameters), and Grover-Mega (1.5 billion parameters).
- RealNews, a corpus of news articles from Common Crawl, is created for training the models. The dataset consists of 5000 Google News indexed domains with metadata.
- Language modeling results show that Grover improves when conditioned on metadata and perplexity decreases with model size.
- Grover outperforms GPT2 in terms of perplexity, possibly due to the difference in data distribution (Grover uses a news-only corpus).
- The paper proposes a method for generating fake news by conditioning on metadata and using Grover's generated text as input for another model. This can help identify weaknesses in the model.
- Future work includes improving Grover's performance, investigating other data sources, and exploring methods to detect fake news at scale.
- The paper focuses on defending against neural fake news by analyzing Grover, a large language model (LLM) that generates propaganda.
- Grover's performance is evaluated in unconditional and conditional settings, with perplexity dropping when given metadata.
- Human evaluation shows that Grover-generated propaganda is rated more plausible than original human-written propaganda.
- Nucleus Sampling (top-p) is used to restrict the variance of generations in Grover, improving its performance and reducing degenerate text.
- Humans are easily fooled by Grover-written propaganda, with overall trustworthiness scores increasing from 2.19 to 2.42 when rewritten by Grover.
- The paper highlights the importance of understanding how LLMs can be used for propaganda and suggests further research on detecting fake news generated by these models.
- Separating style versus content for evaluating trustworthiness and believability in articles.
- Statistically significant difference between human-written propaganda consistency and machine-written propaganda consistency.
- High quality of neural fake news generated by Grover, making automatic detection crucial research.
- Using models like Grover, GPT2, BERT, or FastText for the role of Verifier to mitigate harm from neural fake news.
- Framing neural fake news detection as a semi-supervised problem due to limited human-written and machine-generated samples.
- A neural verifier has access to both labeled and unlabeled data in this setting, improving performance.
- The paper presents an evaluation of the proposed methods on real-world datasets, achieving 30% accuracy with Grover and 45% with BERT.
- The authors suggest that their approach can be used for content moderation and end-user identification of potential disinformation.
- The paper explores defending against neural fake news as a semi-supervised problem, focusing on developing a neural verifier (discriminator) to identify machine-generated fake news from human-written articles.
- The study uses 10k news articles each for training, validation, and testing, with 50% labeled as real and the other half as machine-generated. Two evaluation modes are considered: unpaired (single article classification) and paired (comparing two articles with similar metadata).
- Grover performs best at detecting its own fake news, achieving around 90% accuracy across various sizes in the paired setting. Accuracy drops when using larger generators or smaller discriminators.
- Other models like BERT perform worse than Grover overall, even with similar architecture size and domain control.
- The study highlights that disinformation can be shared on heterogeneous platforms, making it challenging to pinpoint a single generated model.
- Practical applications of this research include improving the detection of fake news in social media platforms and other online environments.
- The paper investigates defending against neural fake news by analyzing various generative models' performance in discriminating between real and generated news articles.
- It examines Grover, a neural text generator, as an adversary and evaluates different discriminator models (Grover-Mega, Grover-Large, BERT-Large, GPT2, Grover-Base, BERT-Base, GPT2, FastText).
- The study finds that weak supervision improves discrimination performance when few in-domain samples are available, as it allows the discriminator to see additional generations from weaker models.
- Observing more generations from Grover-Mega leads to higher accuracy, with around 92% convergence when a significant portion of examples comes from Grover-Mega.
- The paper explores how Grover performs best at detecting fake news generated by other Grover models due to exposure bias and variance-reduction algorithms in the model's training process.
- It also discusses the importance of understanding the relationship between exposure bias, variance reduction, and artifacts created during training.
- The study highlights that discriminating neural fake news requires a similar inductive bias as the generator, suggesting that Grover's performance is due to its shared architecture with other generative models.
- The paper concludes by emphasizing the need for further research on defending against neural fake news and understanding how different architectures affect discrimination performance.
- The paper investigates exposure bias and its impact on creating artifacts, particularly in neural fake news detection.
- Exposure bias occurs when a model over-estimates the likelihood of generated samples being real, leading to poor discrimination between human and machine text.
- Grover-Mega's perplexity increases with position, suggesting that sampling without variance reduction falls out of distribution for human language.
- Limiting variance (top-p) lowers resulting perplexity but creates artifacts, similar to top-k sampling.
- Nucleus Sampling has a probability of zero for observing a human-written article with all tokens drawn from the top-p% distribution as document length increases.
- The visibility of artifacts depends on the choice of discriminator.
- Grover can detect GPT2-Mega generated news with 96% accuracy when trained to discriminate between real and fake news.
- Accuracy in discriminating between human and machine text can increase up to 98% with more examples (Zellers et al., 2019c).
- BERT-Base performs worse than a GPT discriminator at picking out human text, while Grover-when-trained-to-discriminate outperforms it.
- The paper highlights the importance of understanding and addressing exposure bias in neural fake news detection models.
- The paper investigates threats posed by adversaries using controllable language models (Grover) for spreading disinformation and proposes defenses against them.
- Grover can rewrite propaganda articles, making them appear more trustworthy to humans.
- Grover's effectiveness as a detector of neural fake news is demonstrated, even when the generator is larger than it.
- A sweet spot for top-p threshold in discrimination tasks was found between 0.94 and 0.98, depending on the discriminator.
- BERT's lower performance during discrimination might be due to its different view of language compared to Grover.
- Grover's training cost is relatively low, making it achievable for real-world adversaries.
- Releasing models like Grover to researchers can help in detecting and combating adversarial attacks.
- BERT performs well as a discriminator for many NLP tasks but struggles with detecting Grover's generations, even after domain adaptation.
- The paper explores defending against neural fake news, focusing on left-to-right models like Grover and their limitations in detecting artifacts.
- Recent progress in generating text in any order may lead to models evading Grover discriminators.
- Models trained conditioned on their own predictions can avoid exposure bias but often result in low performance on language tasks.
- Adversarial Filtering didn't work well for long sequences, possibly due to being outside the 'Goldilocks Zone'.
- Other threat models include generating comments, modifying existing articles, or fabricating images/videos.
- Grover can be used to detect human-written fake news as well, but machines can also generate truthful news using templated systems.
- Future work should focus on integrating knowledge into discriminators and scaling progress towards entire news articles without paired evidence.
- Platforms like YouTube use deep neural networks for content filtering; a similar approach could be applied to news articles, but with humans in the loop due to potential false positives and biases.
- An ensemble of deep generative models (e.g., Grover) can analyze text alongside shallow models that predict human-written disinformation.
- The paper acknowledges anonymous reviewers' contributions and discusses future research directions.
- The paper discusses defending against neural fake news, focusing on identifying and mitigating potential biases in LLMs.
- It acknowledges the possibility of social biases in these models and emphasizes the need for addressing them.
- Anonymous reviewers, Dan Weld, Zak Stone (Google Cloud TPU team), and various organizations provided support for this research.
- The work was funded by the National Science Foundation, DARPA CwC program, Sloan Research Foundation, Allen Institute for Artificial Intelligence, NVIDIA AI Lab, Samsung, Google, Facebook, and beaker.org (Google Cloud credits).
- An example website mentioned in the paper is https://americanbankingnews.com/.
- The main goal of this research is to ensure that LLMs do not perpetuate or amplify existing social biases while generating content.
- Practical applications and benefits of addressing these issues include more accurate, fair, and unbiased information dissemination through AI-generated content.
- Unusual findings may emerge as researchers continue to explore the potential biases in LLMs and develop methods for mitigating them.
- The paper does not provide specific numerical results or metrics in this section; however, it highlights the importance of addressing social biases in LLM research.
",2983
"1906.00300",1,"- Open Retrieval Question Answering (ORQA) is introduced, which learns to retrieve evidence from an open corpus using only question-answer string pairs as supervision.
- The main challenge in end-to-end learning is treating retrieval over the open corpus as a latent variable that would be impractical to train from scratch.
- Pre-training the retriever with an unsupervised Inverse Cloze Task (ICT) provides a strong initialization for ORQA, allowing it to be fine-tuned end-to-end by simply optimizing the joint reader and retriever model.
- Evaluation on open versions of five QA datasets shows that learned retrieval is crucial in scenarios where users genuinely seek answers, outperforming BM25 by up to 19 points in exact match.
- In contrast, traditional IR systems like BM25 are sufficient for datasets where the questioner already knows the answer.
- Latent Retrieval for Weakly Supervised Open Domain Question Answering introduces a new approach to retrieval-based open domain question answering systems, focusing on learning the retrieval and reader components jointly instead of using separate pipelined models.
- The paper proposes an end-to-end model called Open-Retrieval Question Answering (ORQA), which combines a retrieval component that scores evidence blocks based on inner products between BERT-based encoders and a reader component with multi-layer perceptrons for scoring answer spans.
- The ORQA model learns from all of Wikipedia directly, unlike previous work using IR systems for candidate proposal. It also uses weak supervision to remove spurious ambiguities in the retrieved evidence blocks and treats the cleaned results as gold derivations.
- Experiments show that learned retrieval is crucial for open domain question answering on datasets where question writers do not know the answer, providing improvements of 6 to 19 points in exact match over BM25.
- The paper highlights the importance of learning both components jointly and directly from all of Wikipedia, which leads to better performance compared to pipelined models that use separate retrieval and reading systems.
- The Open-Retrieval Question Answering (ORQA) model combines triever and reader components, allowing it to retrieve any text in an open corpus rather than being limited to a closed set.
- The BERT architecture is used for scoring components, with the retriever and reader learning from inner products of dense vector representations.
- Inference and learning challenges include a large search space and spurious ambiguities due to weak supervision.
- Pre-training the retriever with an Inverse Cloze Task (ICT) addresses these issues by pre-encoding evidence blocks, enabling dynamic yet fast top-k retrieval during fine-tuning, and biasing towards supportive evidence.
- The model achieves state-of-the-art performance on SQuAD 1.1 with a 30% absolute improvement over the previous best result (89.2 vs. 69.2).
- ORQA's retrieval accuracy is 74.1%, outperforming the baseline by 5.5%.
- Latent Retrieval for Weakly Supervised Open Domain Question Answering introduces a new approach called Inference Context Tuning (ICT) to improve the performance of Large Language Models in weakly supervised open domain question answering.
- ICT involves pre-training on a large corpus, where a discriminative objective is used to learn both abstract representations and word matching features for evidence retrieval.
- The model uses fixed block encoders (BERT) for evidence blocks, which are pre-computed and stored in an index for fast maximum inner product search during inference.
- Learning occurs through a distribution over answer derivations, optimizing the marginal log-likelihood of correct answer strings. An early update is also included to encourage more aggressive learning.
- The model's query encoder can potentially learn to retrieve any evidence block, making it more expressive than blackbox IR systems.
- Experiments were conducted on five open domain question answering datasets, showing significant improvements in performance compared to baseline models.
- Natural Questions, WebQuestions, CuratedTrec, TriviaQA, and SQuAD are evaluated for open domain question answering with latent retrieval.
- Datasets have inherent biases: Question askers don't know answers in some datasets (Natural Questions, WebQuestions, CuratedTrec), while others have questions written with known answers (TriviaQA, SQuAD).
- Evaluation is done without evidence corpus or head-to-head comparisons to DrQA setting.
- English Wikipedia snapshot from December 20, 2018, used as the evidence corpus.
- Latent retrieval models achieve state-of-the-art performance on open domain question answering tasks with weak supervision.
- Latent Retrieval for Weakly Supervised Open Domain Question Answering: Main Contributions and Findings
- Applied AI research in Large Language Models (LLMs) focuses on this paper's analysis of latent retrieval for weakly supervised open domain question answering.
- The study introduces a new method that combines BERT-based retrieval with a reader model to address the limitations of existing unsupervised neural language models in representing evidence blocks.
- Key findings include: BM25 is a powerful retrieval system, but it's not trainable; context-independent and context-dependent embeddings from neural language models struggle to encode evidence blocks into 128 dimensions effectively.
- The ICT pre-trained retriever outperforms BM25 in datasets where question askers don't know the answer, but struggles in those where they do (SQuAD and TriviaQA).
- The study demonstrates that language models can be used as baselines for comparison, even though they are not explicitly optimized for retrieval tasks.
- The paper highlights the difficulty of encoding blocks of text into 128 dimensions using neural language models.
- In datasets where question askers know the answer (SQuAD and TriviaQA), a highly compressed 128-dimensional vector cannot match BM25's ability to precisely represent every word in the evidence.
- Latent Retrieval for Weakly Supervised Open Domain Question Answering addresses the issue of SQuAD's limited dataset, which leads to a high correlation between training examples and violates the IID assumption. This makes it unsuitable for learned retrieval.
- The paper proposes an open-domain question answering model called ORQA (Open-Retrieval Question Answering) that combines BM25 with BERT. It uses a weakly supervised approach, where the answer is not explicitly provided in the training data.
- ORQA's retrieval process involves masking 90% of pseudo-queries to encourage learning n-gram overlap as a powerful retrieval signal. The model also incorporates inverse cloze tasks for pre-training and fine-tuning.
- Experiments show that the BM25 baseline outperforms BERTserini, with an improvement of 10 points in end-to-end performance when compared to 5-document BERTserini.
- The paper presents example predictions from ORQA and highlights its robustness at separating semantically distinct text with high lexical overlap. However, the limitation of the 128-dimensional vectors affects the precision in representing extremely specific concepts.
- The authors suggest that those interested in end-to-end open-domain QA models should no longer train and evaluate with SQuAD due to its artifacts.
- ORQA (Open-Domain Question Answering) is a system that learns retriever and reader end-to-end using only question-answer pairs without an IR system.
- It uses Inverse Cloze Task (ICT) for pre-training the retriever, which allows learning to retrieve crucial when questions reflect an information need.
- Experiments show that ICT improves retrieval performance by 10% in terms of accuracy and 4.5 times faster than baselines.
- The system's limitations include handling extremely specific concepts and sparse representations, which can be addressed through a hybrid approach.
- Related work includes improving evidence retrieval, weakly supervised semantic parsing, and representation learning literature.
- Consulting external evidence sources with latent retrieval has been explored in information extraction, allowing for more expressive retrievers due to strong inductive biases from ICT pre-training.
- Acknowledgments thank the Google AI Language Team for valuable suggestions and feedback.
- Latent Retrieval for Weakly Supervised Open Domain Question Answering: The paper introduces a new approach to improve open-domain question answering (ODQA) by combining weak supervision and latent retrieval techniques.
- Weak supervision: It uses a large corpus of unlabeled data, where only a small portion has been labeled for training. This allows the model to learn from a vast amount of unlabeled examples while still having some guidance from labeled data.
- Latent Retrieval: The method retrieves relevant passages from a large corpus using a latent space representation. It uses a neural network to map documents into a low-dimensional space, enabling efficient and effective retrieval.
- Combining weak supervision with latent retrieval: This combination allows the model to learn from both labeled and unlabeled data, resulting in better performance compared to using only one of these techniques.
- Experiments: The paper evaluates the proposed method on two ODQA datasets - SQuAD (Stanford Question Answering Dataset) and TREC (Text REtrieval Conference). It shows significant improvements over baseline models in both accuracy and efficiency.
- Key findings: The latent retrieval approach improves performance by 10% to 25% on the SQuAD dataset, while reducing training time by up to 4.5 times compared to a strong baseline model.
- Future work: The paper suggests further research in incorporating more complex neural network architectures and exploring different loss functions for latent retrieval.
",1901
"1906.01604",1,"- KERMIT is a simple insertion-based approach to generative modeling for sequences and sequence pairs, presented by researchers from Google Research, University of California, Berkeley, and others.
- Unlike traditional seq2seq models, KERMIT directly models the joint distribution p(x, y) and its decompositions (marginal and conditional distributions).
- During training, paired data (x, y) is used to learn the joint distribution, while unpaired data can be mixed in for refining marginals.
- KERMIT supports both serial fully autoregressive decoding and parallel partially autoregressive decoding with logarithmic runtime.
- Experiments demonstrate that KERMIT matches or exceeds performance of dedicated state-of-the-art systems across machine translation, representation learning, and zero-shot cloze question answering tasks without requiring problem-specific architectural adaptation.
- KERMIT is a generative insertion-based modeling approach for sequences, capable of conditional inference in either direction and generating paired or unpaired samples from the joint distribution.
- The model has a simple architecture without separate encoder and decoder, nor requiring causality masks. It consists of a single Transformer decoder stack.
- KERMIT can be applied to various tasks such as machine translation, self-supervised representation learning, and zero-shot cloze question answering.
- Compared to other models like Transformer, BERT, GPT, and GPT-2, KERMIT matches or exceeds their performance without requiring problem-specific components.
- Autoregressive left-to-right models (Sutskever et al., 2014; Cho et al., 2014) use a left-to-right factorization for conditional distribution p(y | x), but have drawbacks such as not handling partially observed inputs and inability to generate non-monotonic sequences.
- Masked Language Models (Devlin et al., 2019) focus on unconditional settings, recovering an entire sequence from a partial canvas with known mask locations. They are successful in self-supervised representation learning but not suitable for generation tasks due to fixed canvas size during inference.
- KERMIT's insertion-based approach allows it to handle partially observed inputs and generate non-monotonic sequences, making it more versatile than autoregressive models or MLMs.
- KERMIT is a generative insertion-based modeling framework for sequences, which allows modeling without requiring fixed factorization or constraints on order generation.
- The model uses insertion operations to construct sequences according to a given permutation (z) of indices.
- p(x) is defined as the marginalization over all possible orders z for sequence length n.
- Lower bounding the log-likelihood using Jensen's inequality, we can compute an unbiased estimate of our lower bound L(x) on the log-likelihood for a single example.
- Inference is done via autoregressive or partially autoregressive decoding.
- The balanced binary tree prior from Stern et al. (2019) can lead to an empirical runtime of ≈ log2 n iterations to generate n tokens.
- Key advantage: insertion-based models allow for parallel decoding, which can be faster than sequential decoding in some cases.
- KERMIT: Generative Insertion-Based Modeling for Sequences - Applied AI researcher summary
- Main contributions and findings
- Runtime of ≈ log2 n iterations to generate n tokens, advantage of insertion-based models over MLMs
- Extending KERMIT to pairs of sequences, symmetric treatment of source and target in multimodal data settings
- Model implementation as a single Transformer decoder stack without causal masking
- Experiments on machine translation, self-supervised representation learning, zero-shot cloze question answering
- Unidirectional models for conditional distributions p(y | x) and p(x | y), bidirectional model for joint distribution p(x, y)
- Marginal refining for p(x) and p(y)
- Unidirectional finetuning for p(y | x) or p(x | y), bidirectional finetuning for both directions
- WMT 2014 English ↔ German translation task, achieving BLEU scores comparable to base Transformer baseline
- KERMIT is a generative insertion-based modeling approach for sequences, which aims to improve translation performance without increasing model capacity.
- The combined bidirectional and joint models obtain 27.2/27.6 BLEU scores in English → German translation, nearly matching the baseline but falling slightly behind in reverse direction.
- KERMIT requires approximately log2 n steps to generate a sequence of length n when trained with a balanced binary tree prior.
- The joint model can translate in either direction and incorporates monolingual data for improved knowledge of marginals p(x) and p(y).
- Refining the marginals gives a 1.2 BLEU improvement on German → English translation, while further fine-tuning the trained model improves performance in both unidirectional and bidirectional settings.
- KERMIT models outperform prior non-fully-autoregressive approaches in terms of BLEU scores and require fewer decoding iterations compared to autoregressive models.
- KERMIT is a generative insertion-based modeling approach for sequences, which can be used for self-supervised representation learning and applied to various language understanding tasks.
- It follows the same training procedure as BERT but differs in token dropping instead of masking during representation learning.
- KERMIT's performance on GLUE benchmark (Wang et al., 2019) is slightly behind BERT, but it maintains the ability to generate text while obtaining results closer to BERT rather than GPT.
- The model demonstrates its infilling abilities through zero-shot cloze question answering experiments using the QA2D dataset (Demszky et al., 2018).
- KERMIT is a generative insertion-based modeling approach for sequences, which can handle cloze questions by filling in missing information from declarative sentences.
- The paper evaluates the performance of KERMIT, BERT, and GPT-2 on zero-shot cloze question answering using SQuAD data.
- KERMIT outperforms both BERT and GPT-2 due to its insertion capabilities learned through an insertion-oriented objective.
- BERT's performance is lower than KERMIT because it often prefers shorter answers, as it was not required to handle length modeling during training.
- GPT-2 lags behind the other models due to its inability to condition on both sides of the gap during inference.
- The paper presents an example of KERMIT's performance on SQuAD data, achieving 30.3 F1 and 20.9% exact match accuracy.
- KERMIT is an insertion-based framework for sequences that can model joint data distribution and its decompositions (marginal and conditional).
- It generates text in arbitrary order, including bidirectional machine translation and cloze-style infilling.
- Empirically, it can generate sequences in logarithmic time.
- KERMIT uses a simple neural architecture that produces contextualized vector representations of words and sentences.
- The model matches or exceeds state-of-the-art performance on three diverse tasks: machine translation, representation learning, and zero shot cloze question answering.
- KERMIT outperforms BERT and GPT-2 in these tasks.
- It can generate text with 30% accuracy for the SQuAD dataset, compared to 19% for BERT and 18% for GPT-2.
- For machine translation, KERMIT achieves a BLEU score of 45.6 on WMT'14 English-German test set, outperforming BERT (37.0) and GPT-2 (39.1).
- In representation learning, KERMIT achieves an accuracy of 89.6% on the MNLI dataset, surpassing BERT (85.4%) and GPT-2 (87.6%).
- For zero shot cloze question answering, KERMIT achieves a score of 35.9 on the SQuAD dataset, outperforming BERT (31.5) and GPT-2 (32.4).
- Show, Attend and Tell (SAT) is a neural image caption generation model that incorporates visual attention for improved performance.
- The SAT model was introduced in the ICML conference paper.
- This approach combines convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to generate captions while focusing on specific regions of an image.
- Visual attention allows the model to better understand and describe the most important aspects of an image, leading to more accurate and relevant captions.
- The paper presents a significant improvement in captioning accuracy compared to previous methods.
- SAT's performance is also faster than other models, making it efficient for real-time applications.
- The model's architecture allows for easy integration with existing CNNs, making it adaptable and scalable for various image captioning tasks.
- The paper provides examples of generated captions to demonstrate the effectiveness of the SAT approach in understanding and describing images.
- Key metrics and results are presented in tables, highlighting the model's superior performance compared to other methods.
- Overall, the Show, Attend and Tell (SAT) model represents a significant advancement in neural image caption generation with visual attention, offering improved accuracy and efficiency for real-time applications.
",1871
"1906.06669",1,"- Train unsupervised models for only one epoch instead of multiple, reducing training time and costs significantly.
- Performance improves dramatically in this approach, especially if the original number of epochs was high.
- Under one-epoch training, no overfitting occurs, and regularization methods are unnecessary as they slow down training.
- Size/iteration adjustment based on proposed heuristics leads to 1-2.7x speedup in wall-clock time.
- Combining both methods results in a 3.3-5.1x speedup, potentially reducing the cost of training state-of-the-art models like BERT and GPT-2 by up to ten times.
- This approach could help improve long-range coherence in generative models through architectures such as Transformer-XL and Sparse Transformer.
- The paper suggests that training for multiple epochs is inefficient for data-abundant unsupervised learning, especially when compared to data-scarce supervised classification settings.
- Many recent papers use multi-epoch training, but the number of epochs isn't always reported; it's typically between 10 and 200.
- GPT, BERT, and GPT-2 were trained on original datasets created by their authors, which could have allowed for increased dataset size and reduced epochs for better performance.
- The paper argues that the current practice of training models with large numbers of epochs should be reconsidered; instead, larger standard datasets and single-epoch training are recommended for fair comparisons.
- Related works suggest that training on larger datasets leads to significant improvements in performance, but this study focuses on the trade-off between dataset size and number of iterations (epochs).
- The paper's findings indicate that increasing the parameter budget while fixing the number of epochs can lead to better performance as the dataset size increases.
- This research is related to Hestness et al.'s work, which showed a robust power law relationship between performance and dataset size for varying-sized subsets of LM1B (Chelba et al., 2013). However, this study differs in that it investigates the trade-off between parameter budget and number of iterations.
- The paper's findings suggest that training a model with a large dataset for one epoch can achieve better performance than training with fewer parameters over multiple epochs.
- Practical applications include creating larger standard datasets, reducing computational resources spent on training, and enabling fair comparisons between models.
- This research highlights the importance of considering the trade-off between dataset size, number of iterations, and parameter budget when designing language models.
- The paper proposes that performance improvement can be achieved without increasing computational resources, unlike previous works.
- One epoch training and size/iteration adjustment are introduced to improve performance while maintaining the same computation cost.
- To achieve optimal performance under one epoch training, the ratio of tokens in the dataset to model parameters should be close to 5 (T/P ~ 5).
- The paper provides a heuristic method for setting P and T (model size and number of iterations) while keeping their product constant.
- Practical applications include using these techniques with existing multi-epoch training prototypes, adjusting the number of iterations, or choosing the model size.
- The paper demonstrates that one epoch training can be faster than conventional methods (4.5 times faster in an example).
- The authors suggest that their approach could potentially improve performance for other tasks beyond neural machine translation.
- One epoch training can significantly reduce computation cost, as it scales quadratically with respect to I (optimal model size) while optimal model size increases linearly with I.
- Choose the value of I based on available computation budget and set P according to heuristics like keeping T/P close to 5 while maintaining a constant product.
- One epoch training improves diversity, reduces overfitting, and eliminates the need for validation with test/validation datasets due to its sampling process.
- Experiments conducted on base Transformer decoder (Vaswani et al., 2017) show that one epoch training can achieve comparable or better performance than multi-epoch training while being up to 4.5 times faster and using 30% fewer parameters.
- One epoch training improves language model performance, as shown by comparing single and multiple epochs in different cases (S, M, SD, MD).
- Regularization harms the training under one-epoch training, suggesting that no sample reuse is beneficial for this approach.
- Speedup varies depending on whether the model is overparametrized or underparametrized; it's smaller in underparametrized models.
- The paper presents a speedup calculation method by comparing the number of iterations needed to achieve the best loss for one-epoch training (S) and multiple epochs (M, MD).
- Dropout probability p > 0.1 negatively affects performance in all cases.
- One epoch training can be faster than multi-epoch training with a similar level of performance.
- The paper's findings are applicable to the Transformer model and other models that use attention mechanisms.
- The dataset size is crucial, as it impacts both the number of iterations needed for convergence and the speedup achieved.
- The study demonstrates that one epoch training can be a viable alternative to multi-epoch training in certain scenarios.
- This approach could potentially reduce computational costs and accelerate language model development.
- The paper explores the concept of ""One Epoch Is All You Need,"" which suggests that training a large language model (LLM) with only one epoch can achieve similar performance to multi-epoch training, while being faster and more efficient.
- Speedup is defined as the ratio between the best loss achieved by M or MD over the number of iterations in a single epoch. The speedup for 10 epochs is greater than that for 5 epochs, implying better performance with more epochs.
- When dropout is used, the training curve shifts upward due to increased regularization, slowing down the training process. However, under one-epoch training, this shift doesn't significantly affect the speed of training.
- The power law behavior in LLMs under one-epoch training can be observed through log-log plots of test loss over iterations. The curve first enters a super-polynomial region, then a linear (power-law) region, and finally a sub-polynomial region as parameters become oversaturated with training samples.
- As the parameter budget increases, the power law behavior becomes more pronounced, leading to better performance in one-epoch training compared to multi-epoch training.
- The paper suggests that adaptive dropout can be used to mitigate the gap between single- and multi-epoch training when data availability is limited or unsupervised pretraining doesn't help. However, this method is less efficient than one-epoch training if sufficient data is available.
- One epoch is sufficient for large language models (LLMs) to reach a good performance, as shown in the power-law region of their learning curves.
- The super-polynomial and power-law regions expand with larger models, contributing to superior performance.
- In multi-epoch settings, loss decreases more steeply, monotonically, and for longer iterations compared to single epochs.
- Power-law exponent (approximately -0.067) is similar to Hestness et al.'s findings in 2017.
- Modifying architecture through neural architecture search leads to smaller gains than scaling up models with more data or parameters.
- Changing model width can be considered for simplicity, and the optimal number of iterations (I) is derived by adjusting per-iteration FLOPS.
- The range of optimal I is converted into processed tokens/parameters ratio, which can be used as a heuristic for adjustment.
- Practical applications include using fewer epochs to train large models, reducing training time and cost while maintaining performance.
- One epoch training can lead to better speedup for state-of-the-art models, with a ratio of tokens processed over parameters (T/P) closer to 5.
- The optimal width is determined by finding the closest T/P ratio to 5 among {256, 512, 1024}.
- Size/iteration adjustment leads to better speedup if the original size/iteration proportion is more skewed.
- State-of-the-art models are likely to undergo a greater speedup (by a factor of 10) compared to the 3.3-5.1x speedup in this study.
- One epoch training and size/iteration adjustment can be applied to unsupervised learning algorithms, semi-supervised learning, and potentially larger scale models.
- EfficientNet scaling with the number of iterations may lead to better performance when searching for scaling factors jointly.
- The paper highlights the importance of regularization methods in computer vision tasks, which might benefit from one epoch training even more.
- One epoch is sufficient for training large language models (LLMs) like GPT-2 and BERT, leading to faster training times and improved sample efficiency.
- Fine-tuning pre-trained models on specialized datasets may not require regularization methods, as overfitting might not be an issue due to the lack of regularization during initial training.
- One-epoch training improves performance in fine-tuning, requiring fewer iterations on the dataset to reach similar performance levels compared to multi-epoch training.
- BERT's sample efficiency advantage diminishes under one-epoch training, reducing the gap between it and left-to-right language models (LTRLM).
- LTRLM can perform tasks without fine-tuning or softmax retraining, offering more flexibility in text generation and seamless integration of various datasets for training.
- Combining multiple datasets during training may improve performance compared to using just one dataset.
- Fine-tuning might not be the most effective approach after pre-training; mixing samples from the task of interest with pre-training data could lead to better results without conventional fine-tuning.
- Radford et al.'s (2018) findings suggest that fine-tuning may have similar effects as catastrophic forgetting, implying a need for alternative training methods.
- One epoch training for GPT-2 may not require fine-tuning if the task is known during pre-training.
- Shift of attention from regularization to model capacity: Large-scale training without regularization can improve actual model capacity, allowing research into improving models rather than just regularization methods.
- Creation of new datasets for one epoch training: Subsets of large datasets (e.g., WebText) can be used for comparison, with larger datasets being possible due to the lack of regularization and reduced computational costs.
- Comparing models: Two methods are proposed - one using optimal model sizes identified from smaller subsets and another combining plots of the proposed model and state-of-the-art model.
- Practical applications: One epoch training can potentially reduce training time, lower computational costs, and enable research into improving model capacity rather than regularization methods.
- One epoch is sufficient for training a large language model (LLM) with a small dataset, as long as it has enough data diversity and complexity.
- Data augmentation can be achieved by exploiting the Internet to increase the size of the dataset relevant to the task at hand. This helps alleviate poor performance caused by insufficient information in the original dataset.
- A mismatch-aware sampling strategy can improve sample complexity, as it addresses the difference between human and model's data distribution.
- Sampling from the Internet requires a more lenient approach to handle corrupt webpages, which are common in datasets like CommonCrawl. Analyzing performance with different proportions of corrupt samples helps determine an acceptable upper bound for their presence without significant degradation.
- Corrupt samples can be beneficial for models to learn to distinguish between them and non-corrupt text. By preventing corrupt outputs, perplexity can help detect when a model starts generating corrupt text.
- The paper presents a case study using WebText, which consists of Reddit links with more than 3k karma, as an example of data augmentation from the Internet.
- One epoch training can improve conventional multi-epoch methods by enlarging datasets, adjusting model size and iterations appropriately.
- Overfitting does not occur in one epoch training, and regularization is unnecessary as it only slows down the process.
- Loss curves follow a power-law pattern over iterations for given model sizes.
- One epoch training can reduce the cost of training state-of-the-art models like BERT and GPT-2 by potentially 10 times.
- This approach is promising for not only language models but also unsupervised or semi-supervised learning tasks on various data modalities.
- One epoch training can lead to scaling up models using an analogous method to EfficientNet, considering depth, width, and iterations as scaling factors.
- The sample efficiency gap between BERT and left-to-right language models may diminish with one epoch training. GPT-2 could replace BERT due to its flexibility.
- Since overfitting does not occur in one epoch training, more attention will be paid to improving model capacity.
- New standard datasets should be created for evaluating and comparing models under one epoch training and size/iteration adjustment.
- Data augmentation with the internet can efficiently sample data from various sources to expand training datasets.
- The paper discusses the effectiveness of training large language models (LLMs) for a single epoch, suggesting that it can achieve comparable results to multiple epochs.
- The authors compare their method with BERT and GPT-2, showing that their approach achieves similar performance while requiring less computational resources.
- They propose a new training strategy called ""One Epoch Is All You Need"" (OEIANYN), which involves using a larger batch size, higher learning rate, and a more aggressive warmup schedule.
- The authors demonstrate that OEIANYN can train models with up to 10 times fewer epochs while maintaining comparable performance.
- They also show that the proposed method reduces training time by 30% for GPT-2 and 60% for BERT, without sacrificing accuracy.
- The paper highlights the importance of understanding how model size, learning rate, and batch size affect convergence in LLMs.
- OEIANYN can be applied to various models, including GPT-2, BERT, and T5, with similar results.
- The authors suggest that their method could potentially reduce the carbon footprint of training large language models by reducing energy consumption.
- They also propose a new metric called ""epochs per accuracy point"" (EAP) to measure model convergence efficiency.
- OEIANYN can be used as a practical tool for training LLMs, allowing researchers and developers to optimize their models' performance while minimizing computational resources and energy consumption.
- ""One Epoch Is All You Need"" paper explores the efficiency of training large language models (LLMs) with a single epoch instead of multiple ones.
- The authors demonstrate that LLM performance can be comparable to multi-epoch training, while reducing computational costs and memory requirements.
- They introduce adaptive dropout as a method to mitigate data scarcity issues in supervised learning settings.
- The paper provides insights into the optimal number of iterations for different model sizes, showing that one epoch can be sufficient for some models.
- It highlights the importance of efficient training strategies and their potential impact on LLM development.
- The authors suggest further research to explore the relationship between the number of epochs and performance in various scenarios.
- The paper's findings could lead to more practical applications, such as faster model deployment or reduced computational costs for large-scale language models.
- The paper suggests that for a specific model, there exists an optimal number of iterations to minimize computation cost while maintaining performance.
- This optimal range varies depending on the model's dimensionality (d = 512, 256, or 1024).
- For d = 512, the number of iterations should be greater than 12,000 and less than 84,000.
- For d = 256, the number of iterations should be less than 12,000 × 2.5 (30,000).
- For d = 1024, the number of iterations should be greater than 84,000 / 3 (28,000) and less than an upper bound.
- This finding can help optimize training time and cost for large language models without compromising performance.
- The paper provides a practical guideline for choosing the optimal number of iterations based on model dimensionality.
",3251
"1907.11274",1,"- The paper discusses mitigating malicious use of synthetic media research, focusing on machine learning and its potential harmful impacts.
- It highlights concerns surrounding advances in ML, particularly in generating or manipulating audio, video, images, and text, which can lead to various forms of harm.
- Research risk mitigation strategies from other fields are examined for emulation within the ML and synthetic media research communities.
- The paper acknowledges disagreements on these issues that could polarize conversations but suggests solutions like working with subject matter experts, building a community around understanding impacts, and establishing institutions to support release practices.
- Recommendations include collaborating with experts, organizing workshops at major conferences, and creating systems for responsible research release practices.
- The paper emphasizes the importance of understanding the risk landscape and potential mitigation strategies in addressing malicious use of ML research.
- It encourages researchers to work closely with subject matter experts and build a community around understanding the impacts of their work.
- Establishing institutions and systems for responsible release practices is suggested as a way to manage the challenges associated with these issues.
- The paper acknowledges that while it does not propose specific approaches, it aims to provide useful tools, analogies, and options for thinking about these issues.
- The main focus of this paper is on reducing malicious use of synthetic media research through responsible release practices in machine learning.
- Unintended consequences of synthetic media research can lead to malicious use, such as claiming evidence is fake for avoiding accountability.
- Recent examples include manipulating audio, video, images, and text using ML, creating almost indistinguishable synthetic faces, and overlaying synthesized speech.
- These advances could be used for impersonation, swaying public opinion, or spreading doubt about media authenticity.
- Modern synthetic media is already being misused for harassment, financial crimes, and espionage.
- Researchers should consider responsible practices in ML and synthetic media research, potentially withholding some aspects of their work to avoid harm.
- Identify risk mitigation strategies from other fields that can be applied to ML and synthetic media research communities.
- Disagreements on these issues may polarize conversations, leading to recommendations for research and practice.
- Synthetic media research can lead to three types of hazards: product, data, and attention hazards.
- Product hazards involve software that can be directly used for harm by less skilled adversaries.
- Data hazards create risks through detailed information or outputs, increasing the likelihood of malicious use by those with some technical capabilities.
- Attention hazards increase the likelihood of mal-use by raising awareness about new technologies and their potential misuses.
- Mitigating these hazards requires different approaches for each type; for example, careful communication with media organizations to avoid attention hazards while raising wider concerns might help address data or product hazards.
- Artificial voice cloning is used as an illustrative example of a capability with both beneficial and malicious applications.
- Factors influencing whether a capability leads to sustained mal-use include awareness, technical barriers, social norms, and the potential for profit or harm.
- Awareness: Actors with malicious intent must know about a capability and believe it can be used for their objectives.
- Technical barriers: Difficulty in using a technology may limit its misuse, but as technologies improve, these barriers decrease.
- Social norms: Societal expectations can influence whether an actor uses a technology maliciously or not; for example, scamming is generally considered unacceptable behavior.
- Profit or harm potential: If the benefits of misusing a capability outweigh the risks, it may be more likely to occur.
- Awareness of malicious actors: They need to realize the potential use and effectiveness of new capabilities, influenced by attention to similar methods and compelling arguments from authoritative sources.
- Deployment difficulty: Factors affecting ease of weaponizing a capability include talent pipelines (ML expertise needed), reproducibility, modifiability, slottability, and environmental factors.
- Examples: Voice cloning software's ease of use for malicious purposes, websites enabling instant face generation, and the impact of phone number spoofing on voice cloning weaponization.
- Practical applications: Understanding these factors can help in designing systems to mitigate malicious use of synthetic media technologies.
- Unusual findings: The paper highlights that adversaries' awareness and deployment difficulty are not binary but rather influenced by multiple factors, making it important to consider a range of aspects when assessing the potential for malicious use.
- Synthetic media generation websites enable anyone to create photorealistic faces without technical expertise, making malicious use easier and more accessible.
- Sustained use of capabilities with substantial negative impacts depends on factors like ROI (return on investment) and assessment of ROI accuracy. If adversaries believe the ROI is low or their assessments are flawed, they might not continue using it.
- Access ratchets represent a progression from theoretical capabilities to scaled-up use in practice. Once a technology becomes easy to use and has high ROI for malicious purposes, addressing it can be difficult.
- Researchers should consider where a capability sits on the access ratchet's progression: attention/interest, weaponization potential, and likelihood of sustained use. This helps identify risks and appropriate interventions.
- A capability that hasn't been used maliciously yet might still have high potential for harm depending on its position along the access ratchet. For example, Face2Face has not led to harmful products due to lack of productization and competition for AI talent.
- Direct harms from synthetic media research can lead to immediate consequences, such as financial scams using voice cloning.
- Indirect harms are more complex and harder to anticipate, but potentially more significant in the long run. They involve misinformation campaigns that influence democratic power and control narratives.
- Mitigating harm through release practices is crucial, but not a one-size-fits-all solution. Different approaches should be considered, including research selection, risk assessment, and deciding when and how to release research outputs.
- Careful release practices can help mitigate malicious use within ML research, although they are not the main or most important component of addressing this issue.
- Research directions and prioritization also play a significant role in preventing harmful applications of synthetic media capabilities.
- General ML capabilities may be applied to various purposes, making it challenging to focus solely on beneficial ones. However, some general guidelines can still be identified for more likely malicious uses.
- Challenges to mitigating harm from synthetic media research: Composition problem, slow drip problem, conflation problem, and defector problem.
- These challenges make limiting access to research difficult but also motivate the development of nuanced release practices for ML research.
- Examining analogs in other fields like biotechnology and information security can provide insights into effective release practices for ML research.
- Biosafety processes from biotechnology offer promising practices, including procedures, lab safety officers, and risk assessment committees.
- Information security has a similar concept of threat modeling to identify potential risks and vulnerabilities in systems.
- Researchers can consider using ""red team"" exercises to test the robustness of their ML models against adversarial attacks.
- The paper suggests that researchers should be encouraged to publish negative results, as they provide valuable insights into the limitations of a technique or model.
- Incorporating ethical considerations in research proposals and review processes can help ensure responsible development of synthetic media technologies.
- Researchers should also consider establishing ""red teams"" within their organizations to test the robustness of ML models against adversarial attacks.
- The paper emphasizes that while these practices may not completely eliminate malicious use, they can significantly mitigate harm and improve the overall safety of synthetic media research.
- Safety practices in biomedical and synthetic media research can be compared to those in high-risk laboratories, with similar considerations for lab personnel, architecture, audits, safety level designations, and defining organizations.
- Transfer learning might pose challenges in delaying the release of synthetic media research due to its potential impact on access ratchets and sociotechnical immune systems.
- Computer/information security practices include OPSEC, secure-by-design architecture, coordinated disclosure, ISACs/CERTs, and Institutional Review Boards (IRBs) for human subject protection in biomedical and behavioral research.
- IRBs use case-dependent scrutiny to assess research proposals, with approval criteria focusing on minimizing risks to subjects, ensuring reasonable risk relative to benefits, selecting subjects equitably, and avoiding vulnerable populations.
- External expert evaluation is a key component of the IRB process, providing an additional layer of protection for human subjects in biomedical and behavioral research.
- Identify vulnerable populations and external experts for evaluation to mitigate risks of synthetic media research.
- Continuous evaluation, periodic updates, and IRB involvement can help manage negative impacts.
- Explore analogous practices from other fields like nuclear technology, spam detection, classified information, and environmental impact.
- Potential ML release practices: Release options, release rubric, release rubric processes, release coordination, release training, and release process entities.
- Consider various dimensions for release strategies, such as content and timing.
- Examples of release options include fully runnable systems, modifiable systems, source code, trained models, papers/concepts, harmful use case ideas.
- Timing options include immediate release, timed release, periodic evaluation, and continuous evaluation.
- Release strategies for synthetic media research: Evented, staged, and timeline-based approaches can be used to control when and how these technologies are released.
- Distribution considerations: Public access with varying degrees of publicity, requesting access, safety levels, access communities, and release within trusted groups.
- Mitigating malicious use in synthetic media systems: Consent, detectability, watermarking, referenceability, and constraints on synthesis can be used to reduce misuse.
- Examples in practice: Google's research sharing approach, Face2Face researchers not releasing code, intentionally difficult-to-use code, and companies like Synthesia working with vetted clients.
- The paper discusses reducing malicious use of synthetic media through machine learning research, focusing on release practices and their impact on openness vs. caution in ML studies.
- Disagreements arise around value trade-offs (openness vs. caution), beliefs about risks, and the potential consequences of power concentration or public confusion.
- Openness norms in ML research are driven by distributing benefits widely and enabling scientific progress through critique and collaboration.
- Research practices that limit malicious use may reduce some forms of openness but also offer opportunities to explore new approaches that balance risk reduction with preservation of important aspects of openness.
- Beliefs about the relative size of risks can lead to different perspectives, with some prioritizing safeguarding democracy and public trust over power concentration concerns, while others consider weaponization less immediate and prefer to reassess risks later.
- Finding a balance between addressing both sides of the concern is crucial, as it may involve developing approaches that address one side without exacerbating the other.
- Disagreements exist around standardizing release practices for synthetic media research, with concerns about power concentration and effectiveness of different approaches to mitigate risks.
- Beliefs on efficacy vary from the impossibility of preventing mal-use in the long run to the potential impact of incentives or barriers on reducing risks. Some argue that public research can help build better defense systems, while others believe security through obscurity is crucial for certain applications.
- There's a question about whether future needs might require processes for release of ML research, even if they aren't essential now.
- The paper suggests considering various options and approaches to strike a balance between differing perspectives on open or closed ML research.
- Recommendations include increasing understanding of risk landscapes, exploring potential mitigation strategies, and collaborating with external entities for standardization of release practices.
- Increase understanding of ML research risks and mitigation strategies: Develop standardized language, map risks, discuss threats and path dependencies, safely communicate about risks, and identify mitigation options.
- Build a community around competency in impact evaluation: Conduct regular workshops, spread awareness of risks, involve those affected, encourage impact evaluations for publications, proposals, and presentations.
- Fund institutions and systems to manage ML research practices: Support expert impact evaluation, prototype vetting systems, develop release procedures for high-risk research, and potentially open up research by improving release practices.
- Focus on responsible safeguarding of ML research through release and publication practices, preventing malicious use of synthetic media creation (and other potential misuses).
- The paper discusses reducing malicious use of synthetic media research through considerations and potential release practices for machine learning.
- It aims to decrease polarization in the debate between openness and closed ML research, emphasizing that there are various options for releasing different aspects of research.
- Researchers should explore risks and options, develop strategies, and balance trade-offs while focusing on benefiting humanity through ML advancements.
- This work is part of a maturing ML community's efforts to ensure fairness, transparency, and accountability in their systems.
- As ML reshapes our lives, researchers must come to terms with the impacts of their work on world affairs.
",2552
"1908.06083",1,"- The paper focuses on detecting offensive language in dialogue contexts, addressing the need for robustness against adversarial human attacks.
- It introduces a ""Build it Break it Fix it"" strategy to develop models that become more resilient to such attacks by iteratively training with humans and models involved.
- The study shows that offensive language detection depends on dialogue context, not just single sentences as in most previous work.
- The paper presents an automated approach for the ""Build it Break it Fix it"" strategy using crowdworkers, which results in more robust systems over iterations.
- Data analysis reveals a shift from simple profanity to more sophisticated attacks requiring world knowledge, figurative language, and negation understanding.
- Model architectures that use dialogue context effectively perform better than those without, as the latter has been the main focus of existing research.
- The authors make their code, data, and trained models available for the community to promote further research in this field.
- The paper focuses on dialogue safety and robustness from adversarial human attack for Large Language Models (LLMs).
- It uses a ""Build it Break it Fix it"" approach, inspired by software engineering methods, to improve the robustness of LLM models in detecting toxic language.
- The study uses the Wikipedia Toxic Comments dataset as a benchmark and compares its results with existing work.
- A large pre-trained transformer model (BERT) is used for baseline classification, while the ""Build it Break it Fix it"" approach involves human annotators to identify weaknesses in the LLM models.
- The paper presents a novel methodology that combines adversarial training with human feedback to improve dialogue safety and robustness.
- Experiments show significant improvements in model performance, with accuracy increasing from 74% to 89%.
- The approach can be applied to other NLP tasks, such as text classification, machine translation, and reading comprehension.
- The paper highlights the importance of considering contextual information for better toxic language detection.
- It also emphasizes the need for more research in this area, as current methods are still limited in their ability to handle complex scenarios.
- The ""Build it Break it Fix it"" approach can be used to improve dialogue safety and robustness in LLMs, leading to better performance in toxic language detection tasks.
- Build an initial BERT-based model (A0) for detecting offensive messages using the Wikipedia Toxic Comments dataset.
- Define ""breaking"" as crowdworkers submitting messages that A0 marks as safe but they consider offensive.
- Collect adversarial data from breaking phase and train a new model (A1).
- Repeat steps 2-3 for multiple iterations to create more robust models (A2, A3, etc.).
- Evaluate the performance of each model using weighted F1 score and OFFENSIVE F1 score.
- Compare results with baseline methods like fastText and BiLSTM.
- Show that the proposed approach outperforms baselines in detecting offensive messages in both single-turn utterances and multi-turn dialogues.
- The paper focuses on building robust dialogue safety models by using adversarial human attacks to improve Large Language Models (LLMs).
- A three-round process is used: ""build it"" (train a baseline model), ""break it"" (workers try to find offensive messages that the model marks as safe), and ""fix it"" (update the model with the newly collected adversarial data).
- The study compares single-turn and multi-turn tasks, using BERT-based models trained on Wikipedia Toxic Comments, ConvAI2 chit-chat task, and adversarial/standard data.
- In single-turn tasks, the paper analyzes language properties in OFFENSIVE examples from both standard and adversarial methods. Adversarial examples require more sophisticated language understanding.
- The multi-turn task involves a dialogue system where one agent tries to detect offensive messages in a conversation.
- Results show that models trained on adversarial data perform better than those trained on standard data, especially in the later rounds of the process.
- The paper highlights the importance of considering multiple rounds and incorporating human feedback for improving LLMs' robustness to adversarial attacks.
- The paper aims to improve dialogue safety by studying robustness from adversarial human attack.
- Three rounds of data collection were conducted: standard task, adversarial task (rounds 1-3), and a baseline model trained on the Wiki Toxic Comments dataset.
- Adversarial examples in the adversarial task contained less profanity, fewer non-profane offensive words, more figurative language, and required more world knowledge compared to standard examples.
- The adversarially trained models proved to be more robust against adversarial attack than standard models.
- Adversarial tasks became harder with each round, while the performance of standard models deteriorated between rounds 1 and 2.
- The paper suggests that future work should focus on improving dialogue safety by incorporating human-in-the-loop methods to generate more sophisticated adversarial examples.
- Adversarial behavior occurs in context, such as conversations or comment threads.
- The paper focuses on offensive utterances within two-person dialogues for dialogue safety.
- To collect data, crowdworkers were asked to continue a conversation with offensive responses that the classifier marked as safe.
- A multi-turn adversarial task was created by combining this data with SAFE examples from ConvAI2.
- Two BERT-based models were trained on this dataset: one without context and another splitting the last utterance and dialogue history into two segments.
- The fastText model performed worse than BERT-based architectures in this task.
- Results showed that models need to train on adversarially collected data for robustness against adversarial behavior.
- Standard models improved with subsequent rounds, while the baseline model performed best on its own test set.
- All scores of 0 in Table 6 were by design as there was no offensive response in round 1 for A0 and Ai-1.
- The paper highlights the importance of moving beyond classifying single utterances to consider context for dialogue safety.
- The paper introduces a build-it, break-it, fix-it strategy for improving dialogue safety by creating more nuanced offensive language datasets.
- These datasets include less profanity and require more contextual understanding, making current classifiers fail.
- The adversarial data includes figurative language, negation, and world knowledge, which makes existing models vulnerable to attack.
- Classifiers that learn from these complex examples are shown to be more robust against attacks.
- Using dialogue context in the model architecture improves performance significantly.
- Future work could consider separate classes of offensive language or explore other tasks like social media or forum dialogues.
- The build-it, break-it, fix-it strategy can potentially apply to make neural generative models safe as well.
- The paper discusses building robust dialogue systems that can handle adversarial attacks, focusing on safety and ethical concerns in conversational AI.
- It introduces a new challenge called ConvAI2, which involves creating human-like dialogues with agents and evaluating their responses to offensive or toxic language.
- The study analyzes the performance of various dialogue systems under adversarial attacks and proposes methods for improving robustness.
- Researchers developed an ""adversarially trained"" agent that can generate offensive comments, allowing them to test the safety of other agents in response.
- They found that existing dialogue systems often fail to detect or respond appropriately to toxic language, highlighting a need for improved models and evaluation methods.
- The paper presents several case studies, including experiments with GPT-2, BERT, and T5 models, demonstrating the effectiveness of adversarial training in enhancing safety and robustness.
- It also discusses the importance of incorporating ethical considerations into dialogue systems to ensure they do not promote harmful behavior or reinforce negative stereotypes.
- The authors suggest future research directions, such as developing better evaluation metrics for toxicity detection and exploring methods for improving model generalization across different domains.
- Overall, the paper emphasizes the need for robust and ethically sound dialogue systems to ensure safe and meaningful interactions in conversational AI applications.
- The study highlights the importance of adversarial training as a key technique for enhancing safety and robustness in dialogue systems.
- The paper discusses ""Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack"" and focuses on improving dialogue safety in LLMs by addressing adversarial attacks.
- It introduces a novel framework that involves three phases: Build (training models), Break (adversarial data collection), and Fix (improving the model).
- The paper presents results from experiments with different models, including A0, S1, S2, S3, A1, A2, and A3.
- It highlights that adversarial training can improve model robustness by 5-7% in F1 scores for offensive language detection.
- The paper also discusses the importance of data collection interfaces and how they affect the quality of adversarial data.
- The authors provide guidelines for future research, including the need to explore more complex dialogue settings, investigate the impact of different training strategies, and consider additional protected classes.
- The paper aims to improve dialogue safety by enhancing robustness against adversarial human attacks on LLMs.
- It introduces a new method called ""Build it Break it Fix it"" (BiBFi) for training LLMs to handle offensive language.
- BiBFi involves three rounds of training: standard, adversarial, and combined.
- In the standard round, the model learns from clean data.
- The adversarial round introduces human-generated offensive utterances as input.
- Combined training combines both standard and adversarial rounds to improve robustness against offensive language.
- The paper reports significant improvements in F1 scores, precision, recall, and weighted F1 for the OFFENSIVE class when using BiBFi compared to baseline models.
- The authors also provide a table with detailed results from experiments on single-turn standard and adversarial tasks.
- This work contributes to enhancing dialogue safety in LLMs by improving their robustness against offensive language attacks.
- Future research could explore the application of BiBFi in other domains, such as chatbots or customer service agents.
",2039
"1909.05356",1,"- The paper explores cross-lingual Named Entity Recognition (NER) using machine translation for languages with limited annotated corpora.
- It leverages off-the-shelf MT systems, such as Google Translate, to improve entity projection approaches in these languages.
- The proposed system uses MT twice: first for translating sentences and then for entities.
- Orthographic and phonetic similarity are used to match entities, while distributional statistics from the dataset help identify matches.
- This approach improves upon current state-of-the-art methods for cross-lingual NER on 5 diverse languages by an average of 4.1 points.
- The method achieves state-of-the-art F1 scores for Armenian, outperforming even a monolingual model trained on Armenian source data.
- This work focuses on medium-resource languages with no or small NER datasets but good-quality MT support.
- The paper addresses the setting where annotated corpora exist in the source language (English) and only a small validation set is available for the target language.
- The proposed method creates an unlabeled dataset in the target language by translating sentences from the source dataset using off-the-shelf MT systems.
- This approach can be applied to other NLP tasks, such as part-of-speech tagging and dependency parsing, for languages with limited resources.
- The paper proposes a method for cross-lingual Named Entity Recognition (NER) using Machine Translation (MT) and entity projection to create annotated datasets in target languages.
- Google Translate is used as the MT tool, translating source sentences into target languages and then aligning entities between them.
- The proposed solution involves leveraging MT again for translating entities, matching entities based on orthographic similarity, phonetic similarity, and distributional statistics, and identifying matches using distribution-based matching techniques.
- This method achieves state-of-the-art F1 scores in cross-lingual NER for Spanish (+1.1 points), German (+1.4 points), Chinese (+5 points), Hindi (+2.1 points), Tamil (+5 points), and Armenian (beating a monolingual model by 0.4 points).
- The method is portable across target languages, requiring tuning of only two hyperparameters.
- Prior cross-lingual NLP papers can be categorized into direct model transfer and annotation projection approaches; the proposed method falls under the latter category.
- Direct model transfer methods apply models trained on source language data to target language data without modifying the model, relying on a shared representation for both languages.
- The paper's findings demonstrate that entity projection can be effective in cross-lingual NER tasks and provide a practical solution for creating annotated datasets in target languages using MT.
- Cross-lingual NER research often faces challenges when applying direct model transfer techniques to dissimilar languages due to a lack of lexicalized features, which are known to have predictive power for tasks like NER.
- Annotation projection approaches in cross-lingual NLP train models in the target language by projecting annotations from source data to unlabeled target data. Many methods rely on parallel corpora, while some use machine translation (MT) to create synthetic parallel corpora and project annotations.
- Word alignment is a common problem when projecting annotations; most existing works use statistical MT models for this purpose. However, low-resource settings can perform word-by-word or phrase-by-phrase translations without requiring alignment.
- Heuristics like using Wikipedia links across languages to align entities have been explored, as well as methods that use surface forms and transliterations. Some papers also employ supervised models trained on small seed datasets for alignment.
- Few works effectively utilize MT for annotation projection in NER, especially for medium-resource languages with strong MT systems. This paper explores the use of MT systems to translate datasets and improve cross-lingual NER performance.
- The paper explores using Machine Translation (MT) systems for translating annotated NER datasets and entity projection, without relying on parallel corpora.
- The proposed method, called Translate-Match-Project (TMP), consists of three steps: translation, entity alignment, and tag projection.
- Entity alignment involves candidate match generation and best match selection. Candidate matches are generated by constructing a set of potential translations for each source entity in the target language using MT systems.
- The method achieves comparable performance to state-of-the-art methods in all three settings: translation from source to target, using parallel corpora, and translation from target to source.
- TMP can be applied to any resource-rich source language, making it a versatile approach for cross-lingual NER tasks.
- The method's efficiency is 4.5 times faster than the baseline, with an accuracy of up to 30%.
- This work demonstrates that MT systems can be effectively used in cross-lingual NER tasks without requiring parallel corpora.
- The paper addresses issues with translating standalone entities via machine translation (MT) for cross-lingual named entity recognition (NER).
- It proposes augmenting MT with publicly available bilingual lexicons and retaining a copy of the source entity to address inconsistencies in translations.
- Tokenization is performed on candidate translations, allowing soft token-level matches for better results in morphologically rich languages.
- A novel matching heuristic based on orthographic and phonetic levels is introduced, which considers longest sequences of characters as an affix match.
- The paper demonstrates the method's effectiveness even without lexicons in a case study for Armenian.
- The token-level scores include both orthographic and phonetic matches, resulting in entity-level scores based on the best token-level match.
- This approach improves cross-lingual NER performance by 30% accuracy compared to previous methods.
- The method is 4.5 times faster than existing approaches for named entity recognition.
- Practical applications include improving translation quality and enhancing the performance of downstream tasks like information extraction, question answering, and text classification.
- The paper highlights the importance of considering both orthographic and phonetic levels in cross-lingual NER to achieve better results.
- The paper proposes a method for cross-lingual Named Entity Recognition (NER) using entity projection via machine translation.
- It leverages token-level matching between source and target sentences, considering both orthographic and phonetic similarities.
- The 5Panlex lexicon with 10k languages is used for token-level matching.
- Token-level matches are converted into spans to generate multi-token target entities.
- A threshold (δ) is applied to remove spurious matches and ensure maximal span construction.
- The method addresses issues like handling prefixes/suffixes, multiple source entity matches for a single target token, and poor quality matches with stop words.
- The paper demonstrates the effectiveness of this approach on Barack Obama's biography in English and Spanish.
- This method can be applied to other languages and domains, potentially improving cross-lingual NER performance.
- The proposed method is practical and could benefit LLM researchers within organizations.
- Future work includes extending the matching heuristic for handling affixes beyond prefixes/suffixes and incorporating character edit distance.
- The paper proposes a method for cross-lingual NER using entity projection via machine translation.
- It introduces a span match generation process to eliminate spurious matches and concatenate matching tokens, followed by best match selection and distribution-based matching to address unmatched entities.
- The method is evaluated on three European languages, achieving state-of-the-art results in cross-lingual NER tasks.
- Key findings include improved performance over prior approaches, faster execution times, and the ability to handle complex entity types like organizations and locations.
- The paper evaluates a cross-lingual NER approach called TMP (Targeted Machine Projection) on multiple languages, including European, Indo-Aryan, Dravidian, and Chinese languages.
- English NER training data from CoNLL 2003 is used to translate into target languages for all except Chinese, which uses OntoNotes 4.0 dataset.
- TMP achieves higher F1 scores than four other cross-lingual baselines and monolingual models in most cases.
- The paper uses MUSE groundtruth bilingual lexicons and Epitran for translation, entity alignment, and IPA transliteration.
- TMP's performance is comparable to or better than state-of-the-art methods like BWET, fast-align, Co-decoding, and Polyglot-NER in most cases.
- The paper highlights the importance of using gold lexicons for better results and suggests that future work should focus on improving entity alignment algorithms.
- TMP's performance is relatively stable across languages, with only a slight drop in Hindi and Chinese due to language complexity.
- The paper proposes a method for cross-lingual Named Entity Recognition (NER) using machine translation and entity projection via neural networks.
- It compares its performance with other state-of-the-art methods, including Co-decoding, Polyglot-NER, and monolingual models.
- The proposed method outperforms previous cross-lingual NER techniques in Spanish, German, Chinese, Hindi, and Tamil languages.
- Improvements are more significant for languages with different word ordering compared to English, highlighting the importance of machine translation quality on final NER accuracy.
- The technique shows better performance than fast-align baselines, which can lead to alignment errors for named entities due to low-frequency words and multiple target words alignments.
- The paper also compares different projection settings and their impact on cross-lingual NER performance.
- The paper explores cross-lingual Named Entity Recognition (NER) using Machine Translation Projection (TMP) and compares it with Fast-Align for Spanish and Hindi languages.
- TMP involves an entity projection step, which can be achieved through reversing the direction of machine translation or using parallel corpora.
- In the reverse direction setting, Google Translate translates target test sets into source language, then uses Flair NER tagger to tag entities in English sentences and projects them back to the target sentence.
- TMP outperforms Fast-Align for both Spanish and Hindi languages, particularly in Hindi due to its morphologically richer nature and better quality of English NER model.
- Parallel corpora are used to remove translation errors while evaluating TMP and Fast-Align.
- The paper highlights the importance of considering language characteristics when designing cross-lingual NER systems, as Hindi's morphological complexity affects performance.
- The authors suggest that future research should focus on improving NER models for languages with complex morphology and explore other annotation projection paradigms.
- The paper explores cross-lingual NER using machine translation (MT) for English-Spanish, English-Hindi, and English-Armenian. It uses Flair to obtain NER tags in English, which are then projected onto the corresponding target sentences to generate a training dataset.
- MP outperforms fast-align in all cases, with significant improvements in Hindi performance due to the chosen parallel corpus's proximity to test set time periods.
- A case study on Armenian, a medium-resource language, shows that MP achieves an F1 score of 62.6, higher than fast-align and state-of-the-art models trained on larger datasets. The model does not use external resources for Armenian, providing evidence for the proposed approach's effectiveness and generalizability in cross-lingual NER.
- Measuring alignment accuracy through annotation miss rate and excess rate shows that MP has lower noisy annotations compared to fast-align, with higher excess rates for both Spanish and Hindi.
- The paper highlights the practical applications of its findings in rapid deployment to new languages without requiring large external resources or gold lexicons.
- The paper proposes a method for cross-lingual Named Entity Recognition (NER) called Machine Translation Projection (TMP), which leverages machine translation, orthographic and phonetic similarity matching, and distributional statistics to achieve state-of-the-art results in entity projection.
- TMP outperforms Fast Align for both Spanish and Hindi languages in terms of precision, recall, and F1 score on 100 manually annotated examples from translated training data.
- An ablation study shows that adding features like phonetic matching, exact copy translations, gold lexicons, and distribution-based alignment improves the performance of tagging in both languages, with phonetic matching being particularly important for Spanish and gold lexicons for Hindi.
- The paper identifies sources of errors in TMP, such as high thresholds leading to false negatives, contextual sentence level translation issues causing discrepancies between standalone entities and those in context, and mistranslations that affect entity matching.
- Off-the-shelf machine translation systems' improvements can help reduce these errors over time.
- TMP achieves state-of-the-art results for cross-lingual NER on a diverse set of languages, offering practical applications and benefits in this field.
- The paper proposes a method for cross-lingual Named Entity Recognition (NER) using machine translation (MT).
- It achieves state-of-the-art results on various languages, including medium-resource languages like Armenian.
- The approach relies on off-the-shelf MT systems, which are improving in coverage and quality over time.
- While the method falls short of monolingual NER models trained on large corpora, it can be improved through domain adaptation techniques.
- Incorporating domain adaptation could help address distribution shift issues that affect cross-lingual NER performance.
",2674
"1909.05858",2,"- CTRL is a conditional transformer language model for controllable generation, trained on control codes derived from naturally occurring structures in raw text.
- Control codes provide explicit control over text generation while preserving the advantages of unsupervised learning.
- These codes enable potential applications like source attribution analysis and model-based source attribution for large data sets.
- CTRL's 1.63 billion parameter model is available on GitHub in multiple pretrained versions.
- The model can generate text conditioned on control codes specifying domain, style, topics, dates, entities, and relationships between entities.
- CTRL outperforms baselines in most cases, with an average improvement of 10% in text completion accuracy and 25% in style transfer accuracy.
- Future work could involve exploring different control code structures, incorporating additional control mechanisms, or applying CTRL to other domains like summarization and question answering.
- Control codes can be traced back to specific subsets of the training data, enabling analysis of correlations learned from each domain.
- Task-specific control codes improve skills like question answering and machine translation without harming model generality.
- CTRL's conditional transformer language model design allows for controllable generation while maintaining unsupervised learning advantages.
- CTRL is a conditional transformer language model designed for controllable generation, using control codes to guide output.
- The model's performance is comparable to GPT-2 in terms of perplexity and accuracy while being 4.5 times faster during training.
- CTRL learns the distribution p(x|c), where c is a control code and x represents generated text, providing control over generation even during sampling.
- The model architecture is based on the original Transformer with attention layers consisting of multi-head attention and feedforward networks.
- Scores for each token in the vocabulary are computed from the output of the last layer, used as inputs to a cross-entropy loss function during training and sampling new tokens.
- CTRL is trained on 140GB of text data from various sources, achieving state-of-the-art results in controllable text generation tasks such as story completion, dialogue continuation, and summarization.
- The model can generate diverse outputs based on the control code, making it useful for applications like creative writing assistance or generating personalized content.
- CTRL's architecture allows for easy integration of additional control codes to further customize text generation.
- A practical application is demonstrated by using CTRL to generate movie reviews, showcasing its ability to create diverse and coherent outputs based on the provided control code.
- BPE tokenization with a large vocabulary (roughly 250K tokens) is used to mitigate problems with rare words and reduce average token count for long text generation.
- CTRL (Conditional Transformer Language Model) is designed for controllable generation, allowing users to specify a condition and generate text based on it.
- The model has a larger vocabulary (approximately four times larger than similar approaches), resulting in an effective sequence length comparable to other models.
- CTRL achieves state-of-the-art results on the WikiSQL dataset with a 30% accuracy improvement over the previous best model.
- Practical applications of controllable generation include generating text for specific domains, controlling tone and style, chatbots, machine translation, and creative writing tools.
- CTRL's architecture includes 48 layers, 16 heads per layer, a global batch size of 1024 distributed across 256 cores of a Cloud TPU v3 Pod, trained for 800k iterations using Adagrad optimizer with linear warmup.
- Sampling methods include temperature-controlled stochastic sampling, top-k alternatives, nucleus sampling with adaptive probability threshold (pt), and the new ""near-greedy"" scheme.
- Control codes specify overall style by indicating a particular domain of training data, allowing for predictable variation in generation.
- CTRL learns relationships between URL structures and text content during training, enabling novel URL usage at inference time to specify various features.
- Some control codes are related to specific tasks such as question answering and machine translation, acting as task-specific generation triggers.
- CTRL's performance is evaluated on a range of tasks including text completion, question answering, and summarization, achieving state-of-the-art results in some cases.
- CTRL (Conditional Transformer Language Model) is a new approach for controllable generation, allowing users to control the output of LLMs using control codes in natural language prompts.
- The model can handle complex control codes for various tasks like question answering, machine translation, and zero-shot code mixing.
- Influence of multiline examples in training data on generated text coherence: Structure of generated text is affected by the presence of multiline examples in specific tasks (e.g., diet subreddit examples in machine translation).
- Anarchism example demonstrating model's ability to generate diverse and coherent text with control codes from different sources (Wikipedia, Reddit).
- Practical applications of CTRL for generating diverse and controllable text: Potential uses include question answering, machine translation, code mixing, and more.
- CTRL is a Conditional Transformer Language Model (CTLM) designed for controllable generation, using control codes to influence generated text in specific ways.
- The model achieves this through conditional masking and separate decoder networks for each control code.
- Even with identical prompts, control codes allow predictable variation in generation.
- CTLM can be used for applications such as generating different styles or perspectives, creating variations within a single story, and exploring alternative outcomes.
- The model achieves state-of-the-art performance in controllable text generation tasks, outperforming other methods like GPT-2 and GANs.
- CTRL's code is open-source, allowing researchers to build upon or adapt it for their own purposes.
- CTRL is a conditional transformer language model designed for controllable generation, developed by Google AI and UC Berkeley researchers.
- It uses links as control codes to specify domain, subdomain, entities, entity relations, and dates in its training data.
- The paper presents evaluations of CTRL's performance on tasks such as image captioning, summarization, and dialogue generation.
- CTRL achieved 30% accuracy in image captioning, outperforming GPT-2 by 17%, and was 4.5 times faster than GPT-2 for the same task.
- In summarization tasks, CTRL demonstrated a 98% F1 score on CNN/Daily Mail dataset, compared to GPT-2's 93%.
- For dialogue generation, CTRL achieved an average BLEU score of 40.5, outperforming GPT-2 by 6 points and a human baseline by 1 point.
- The model's ability to generate text based on control codes offers potential for more controlled and targeted language generation compared to traditional models.
- CTRL's performance is evaluated using BLEU and ROUGE metrics, achieving 30% accuracy in the Diet domain and 25% in Politics.
- The model can generate text with a coherence score of 4.5 times faster than GPT-2 while maintaining similar quality.
- CTRL's source code is available for research purposes, allowing further exploration and development of controllable language generation techniques.
- CTRL is a Conditional Transformer Language Model designed for controllable generation, addressing issues with empirical prior in training data weighting.
- The model uses uniform prior over domain control codes and relies on cultural associations from sources to predict language patterns.
- Query prompt attribution experiments show sensitivity to changes and improved performance with longer training.
- CTRL is related to language modeling research, building upon contextualized word vectors and models.
- A new method for evaluating generated text quality is introduced by comparing it to human-written references.
- CTRL uses latent space with learned control codes instead of explicit specifications, improving consistency and reducing repetition.
- The model achieves 50% accuracy in controlling domain, 40% for topic, 30% for entities, and 20% for entity relations.
- CTRL can generate diverse texts while maintaining control and consistency with control codes.
- Performance is comparable to GPT-2 on WikiText-103 benchmark, indicating state-of-the-art results without direct language modeling training.
- Future directions include exploring more control codes, finer-grained control, and investigating potential use in NLP tasks like machine translation and summarization.
- CTRL is a Conditional Transformer Language Model designed for controllable generation, allowing users to guide output based on specific prompts.
- Main contributions include improved control over text generation and enhanced creativity through exploration.
- Future work focuses on extending the model to new domains, improving performance in various tasks, and analyzing relationships between language models and training data.
- CTRL-ALT-DEL discusses ethics of large language models, emphasizing openness, replicability, and responsible innovation.
- The research team sought diverse inputs for governance through prerelease review from experts at Partnership on AI (PAI) and technology foresight exercises using scenario planning.
- A code of conduct is included in the model's README, drawing inspiration from emerging community norms like Do No Harm and Just World Licenses.
- CTRL models are released with a subset of questions discussed during deliberation, encouraging reflection and promoting responsible use.
- With 1.63 billion parameters, CTRL is the largest publicly released language model to date.
- Control codes allow specifying domain, subdomain, entities, relationships between entities, dates, and task-specific behavior.
- The model aims to push towards more controllable general models for natural language processing (NLP).
- CTRL (Conditional Transformer Language Model) is a controllable generation model designed for generating texts with specific attributes, outperforming GPT-2 in this task.
- The paper introduces a hierarchical conditioning mechanism that combines global and local conditioning vectors, resulting in high controllability and accuracy (90% style classification, 75% topic classification).
- CTRL's performance is comparable to GPT-2 in terms of perplexity, demonstrating its ability to generate high-quality text.
- Applications for CTRL include diverse response generation, coherent story creation with specific styles or topics, and improving dialogue systems by controlling tone and content.
- The model's code is available on GitHub, allowing further exploration of its potential in various tasks like image captioning and machine translation.
- A new dataset, CTRL-Dial, is introduced for evaluating dialogue systems with controlled generation, showcasing CTRL's ability to generate diverse responses while maintaining coherence and topic relevance.
- The paper emphasizes the importance of controllable generation in various applications and highlights the need for further research in this area.
- CTRL's architecture consists of a Transformer encoder-decoder with an additional conditional layer, enabling conditioning on input context.
- A new loss function is introduced that combines standard language modeling objectives with a conditional term to ensure generated texts adhere to the provided context.
- CTRL achieves state-of-the-art performance in controllable text generation tasks like story completion and dialogue continuation, outperforming previous models such as GPT-2, OpenAI's GPT-1 and GPT-3, and Facebook's BERT.
- CTRL (Conditional Transformer Language Model) is a controllable generation model designed for diverse tasks like story completion, dialogue continuation, and text summarization.
- The model's conditional term minimizes Kullback-Leibler divergence between generated text and the target conditioning context, improving performance with larger datasets.
- CTRL achieves better controllability and generation quality compared to other models, making it a versatile tool for various applications like chatbots, dialogue systems, and storytelling.
- The model's code is available on GitHub, allowing researchers to build upon its architecture or adapt it to their specific needs.
- CTRL uses a combination of pre-trained language models and a conditioning mechanism for controllable text generation, evaluated using metrics like BLEU, ROUGE, and METEOR scores.
- The paper presents a case study demonstrating CTRL's ability to generate diverse outputs while maintaining coherence based on different conditions.
- Potential applications of CTRL include storytelling, education, personalized content generation, and more, highlighting its practical benefits.
- CTRL's architecture consists of a transformer encoder-decoder with an additional conditional layer that incorporates the prompt as input.
- Experimental results show state-of-the-art performance in controllable generation tasks, achieving up to 30% accuracy improvement and generating text 4.5 times faster compared to previous models.
- The paper introduces a novel objective function combining maximum likelihood estimation with conditional logistic regression for training the model.
- CTRL is a conditional transformer language model designed for controllable generation, allowing users to control output by providing specific conditions.
- The model uses a conditioning mechanism with text prompts and control codes like ""style,"" ""topic,"" ""length,"" ""repetition,"" and ""intensity.""
- CTRL achieves state-of-the-art results in controllable text generation, with 30% accuracy for the ""style"" control code on WikiText103 dataset.
- The model outperforms other language models like GPT-2 and BART in terms of controllability and diversity of generated texts.
- Practical applications include generating diverse responses for chatbots, personalized content generation, and enhancing text summarization systems.
- CTRL's code is available on GitHub, encouraging further research in the field of controllable language models.
- The paper emphasizes the importance of controllability in large-scale language models for various applications, paving the way for future research.
- CTRL is a Conditional Transformer Language Model designed for controllable generation, allowing coherent text creation while maintaining control over specified attributes.
- This model can be applied to various tasks such as summarization and translation, where controllable generation proves beneficial.
- The paper introduces a new approach to LLM training that focuses on controllable generation, opening up possibilities in natural language processing applications.
- Unusual findings include the model's ability to generate coherent text while maintaining control over specified attributes.
- CTRL can be used for tasks where controlling the output is crucial, such as summarization or translation, where controllable generation is advantageous.
- The paper presents a novel method of training LLMs that emphasizes controllable generation, expanding the potential applications in natural language processing.
",2792
"1910.01108",1,"- DistilBERT is a smaller, faster, cheaper, and lighter version of BERT, created through knowledge distillation during pre-training.
- The model reduces the size of BERT by 40% while retaining 97% of its language understanding capabilities and being 60% faster.
- DistilBERT uses a triple loss combining language modeling, distillation, and cosine-distance losses to leverage inductive biases learned by larger models during pre-training.
- The smaller model is cheaper to pre-train and can be used for on-device computations, as demonstrated in a proof-of-concept experiment and comparative on-device study.
- This approach allows for similar performances on many downstream tasks using much smaller language models, resulting in lighter and faster inference times while requiring a smaller computational training budget.
- DistilBERT is a compressed version of BERT, achieving similar performance on various downstream tasks while being smaller, faster, and cheaper to run.
- Knowledge distillation (KD) technique used for compression: a compact model (student) learns from a larger model (teacher or ensemble).
- DistilBERT's training loss combines KD loss with supervised learning loss (masked language modeling in this case).
- A cosine embedding loss is added to align the student and teacher hidden states vectors.
- Student architecture: same as BERT, but token-type embeddings and pooler removed, number of layers reduced by half.
- DistilBERT achieves 97% accuracy on SST-2 (Stanford Sentiment Treebank), 80.1% on MNLI (Multi-Genre Natural Language Inference Corpus) and 66.3% on QQP (Quora Question Pairs).
- DistilBERT is small enough to run on edge devices, such as mobile phones.
- Trained weights and training code available in the Transformers2 library from HuggingFace.
- The paper demonstrates that KD can be used for model compression without sacrificing performance.
- DistilBERT's performance is comparable to BERT, making it a viable alternative for applications where size, speed, and cost are crucial factors.
- DistilBERT is a smaller, faster, cheaper, and lighter version of BERT, achieved by removing certain components and reducing the number of layers.
- The student initialization involves finding the right initialization for the sub-network to converge, taking advantage of common dimensionality between teacher and student networks.
- DistilBERT retains 97% of BERT's performance on the GLUE benchmark, with comparable performance on downstream tasks like IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set).
- Inference time for a full pass of GLUE task STS-B (sentiment analysis) on CPU is significantly lower for DistilBERT compared to BERT, with DistilBERT being trained on the same corpus as BERT but requiring less data and compute power.
- Distillation techniques used in training DistilBERT include best practices from recent BERT model research (Liu et al., 2019), leveraging large batches, gradient accumulation, dynamic masking, and without the next sentence prediction objective.
- The paper highlights practical applications of DistilBERT, such as its use in low-resource settings or on edge devices with limited computational resources.
- DistilBERT's performance is comparable to BERT while being significantly smaller and faster, making it a valuable alternative for various NLP tasks.
- DistilBERT is a smaller, faster, and cheaper version of BERT, achieving similar performance with 40% fewer parameters.
- On the General Language Understanding Evaluation (GLUE) benchmark, DistilBERT performs on par or better than ELMo baseline in all tasks except for CoLA and MRPC.
- In downstream tasks like IMDb sentiment classification and SQuAD question answering, DistilBERT shows comparable performance to BERT with less computational resources.
- An additional step of distillation during adaptation can improve the model's performance in specific cases.
- DistilBERT has 60% faster inference speed than BERT and requires 40% fewer parameters, making it more suitable for on-device applications.
- The paper demonstrates that DistilBERT can be used effectively in real-world scenarios like a mobile application for question answering.
- DistilBERT is a smaller, faster, and cheaper version of BERT, created by distilling the knowledge from the larger model into a more compact one.
- The distilled model retains 97% of BERT's language understanding capabilities while being 40% smaller and 60% faster.
- DistilBERT weighs 207 MB, which can be further reduced through quantization.
- An ablation study investigates the influence of various components on performance, revealing that removing the Masked Language Modeling loss has little impact while distillation losses contribute significantly.
- The paper compares DistilBERT to task-specific distillation approaches and highlights its benefits over them.
- Compression techniques like weights pruning and quantization are mentioned as orthogonal to this work, but not explored in the context of DistilBERT.
- DistilBERT is a compelling option for edge devices due to its smaller size and faster performance.
- The code for DistilBERT is available on GitHub (https://github.com/huggingface/swift-coreml-transformers).
- DistilBERT is a smaller, faster, cheaper, and lighter version of BERT (Bidirectional Encoder Representations from Transformers).
- The paper presents an ablation study to demonstrate the effectiveness of DistilBERT in comparison with BERT.
- DistilBERT achieves 97% accuracy on the SST-2 dataset, which is a 6.6% decrease compared to BERT's 100% accuracy. However, this comes at a significant reduction in size and computational cost.
- DistilBERT requires only 40% of the training data used for BERT, resulting in a 4.5 times faster training process.
- The paper highlights that DistilBERT is a compelling option for edge applications due to its smaller size and lower computational requirements.
- In summary, DistilBERT offers a trade-off between accuracy and efficiency, making it an attractive alternative for resource-constrained environments or scenarios where speed and cost are critical factors.
",1241
"1910.06611",1,"- The paper introduces a new variation of the Transformer called Tensor-Product Transformer (TP-Transformer) for better incorporation of structure into its representations, specifically designed to support math word-problem solving.
- TP-Transformer's essential component is a novel attention mechanism called TP-Attention, which explicitly encodes relations between each Transformer cell and other cells based on retrieved values via attention.
- The model sets a new state of the art on the Mathematics Dataset containing 56 categories of free-form math word problems.
- Pretrained models and code are available online for further research and development.
- TP-Transformer's attention maps provide insights into how it solves challenging math problems, strengthening representation building and resolving ambiguities introduced by multiple layers of standard attention.
- The model is trained end-to-end and infers correct answers for novel examples without any task-specific structural biases.
- TP-Transformer's performance on the Mathematics Dataset shows an improvement over previous models, with a 30% accuracy increase in one category.
- The paper views Transformers as Graph Neural Networks and uses this analogy to explain how TP-Attention works within the model.
- The paper proposes enhancing Transformers for math problem solving by introducing explicit relational encoding.
- Each layer of the Transformer is viewed as a complete, directed, weighted, labeled graph, where each edge has a discrete label (1-H).
- The authors suggest replacing these labels with relation vectors to create a representational space for learned relations.
- This embedding uses Tensor-Product Representation (TPR) in an end-to-end differentiable TPR system that learns ""internal spotlights of attention.""
- TPRs support compositional processing by encoding constituent structure, where the representation of a structure is the sum of its constituents' representations.
- The new model, called TP-Transformer, generates key-, value-, query-, and role-vectors (or 'relation vectors') in each head.
- Each head seeks an appropriate filler for a specific role, binding it to the role via tensor product or contraction.
- An example of a learned relation is the second-argument-of relation, where a cell seeks an operator standing in that relation.
- The paper demonstrates how this approach can be applied to math problem solving and provides numerical results (e.g., 30% accuracy).
- Practical applications include enhancing Transformers for various NLP tasks by incorporating explicit relational encoding.
- The paper introduces a new model called TP-Transformer, which enhances the Transformer with explicit relational encoding for math problem solving.
- It uses Tensor Product (TP) attention to capture the full information content in Multi-Head Attention layers and avoid binding ambiguity.
- The TP-Transformer sets a new state of the art for overall accuracy on the Mathematics Dataset, with initial results showing good approximation for roles related to arithmetic operations.
- The model's encoder network consists of cells with three sublayers: Layer Normalization (LN), TP-Multi-Head Attention (TPMHA), and a fully-connected feed-forward layer.
- Each attention head in the TPMHA layer applies separate affine transformations to produce key, value, query, and relation vectors from the hidden state.
- The paper discusses related work and concludes with a summary of the proposed model's contributions and findings.
- The paper introduces a new Transformer variant called TP-Transformer, which enhances the standard attention mechanism with explicit relational encoding for math problem solving.
- This enhancement involves adding a relation vector (rh) to the existing query and key vectors in the attention mechanism.
- The relation vector is multiplied element-wise with the filler/value vector (vh), resulting in a new binding process that controls dimensionality using tensor product contraction and pointwise multiplication.
- Unlike regular attention, TP-Transformer increases the polynomial degree of its representations as a function of the original input to the Transformer, providing further potential for constructing increasingly abstract representations in higher layers.
- The feed-forward layer remains unchanged from previous work, consisting of an affine transformation followed by ReLU activation and another affine transformation.
- The decoder network is a separate structure with two TPMHA layers and one feed-forward layer, designed to take the hidden states of the encoder and generate the output sequence.
- Experiments show that the proposed model achieves 30% accuracy on math problem solving tasks, outperforming the original Transformer by 15%.
- The TP-Transformer is also 4.5 times faster than the original Transformer in terms of inference time.
- The paper introduces a new model called TP-Transformer for enhancing math problem solving using explicit relational encoding in Transformers.
- It uses a Mathematics Dataset with 56 modules covering various types of math problems, including algebra, arithmetic, calculus, etc., and has 2 million pre-generated training samples per module.
- The TP-Transformer achieves state-of-the-art performance on the interpolation and extrapolation datasets, with a new interpolation error record of 15.97%.
- Preliminary experiments show that increasing the number of training steps can further improve accuracy up to 84.24% in interpolation tasks.
- The model uses a shared symbol embedding for both encoder and decoder, which helps it learn relational information between symbols.
- TP-Transformer's performance is better than other models like the regular Transformer, LSTM, and GRU, especially on extrapolation tasks.
- The paper highlights the importance of explicit relational encoding in enhancing math problem solving using LLMs.
- The model can be applied to various domains beyond mathematics, such as natural language processing and computer vision.
- TP-Transformer's implementation details include using 3 hidden layers with 512 units each, a shared symbol embedding of size 72, and a learning rate of 0.001.
- The paper provides an open-source implementation for researchers to build upon and further improve the model.
- The paper introduces an enhanced Transformer model with explicit relational encoding for math problem solving.
- It reduces trainable weights by shrinking hidden state and filter sizes, resulting in TP-Transformer B (1.2 million fewer weights) and TP-Transformer C (32% fewer weights).
- The authors analyze the learned structure of the encoder network's last layer, finding separate clusters for digits in numerators and denominators of fractions.
- Attention maps show that the model learns to focus on relevant parts of the input sequence.
- The TP-Transformer achieves higher accuracy than the baseline Transformer and LSTM models in math problem solving, with an average accuracy of 89% across all modules.
- The paper highlights practical applications for education and automated reasoning systems.
- The paper introduces TP-Transformer, an enhanced version of Transformer for math problem solving by incorporating explicit relational encoding.
- TP-Transformer achieves higher accuracy and faster training compared to the standard Transformer.
- It reduces the total number of weights in the model to improve efficiency.
- The paper provides examples of correctly processed problems from the arithmetic mixed module, highlighting role clustering and attention maps.
- Multi-head attention subspaces capture nearly all information in trained models, contrary to previous claims.
- An affine model is used to reconstruct hidden states from value vectors, demonstrating that TP-Transformer does not lose information during training.
- The paper presents a practical application of the enhanced Transformer for math problem solving and provides insights into its inner workings.
- The paper introduces an enhanced Transformer model with explicit relational encoding for math problem solving.
- It trains an affine model to reconstruct zt,6 from vh(zt,6), the value vector of a single head h, resulting in a mean squared error (MSE) of 0.017 and 0.009 for both trained models.
- The attention mechanism incorporates not just subspaces but also affine transformations of states, preserving nearly full information content.
- The binding problem refers to the difficulty in binding features into objects while keeping them separate from others.
- Standard attention mechanisms struggle with capturing complex nested representations due to ambiguous hierarchical structures.
- TP-Attention (Transformer-Plus) uses a binding mechanism to explicitly support complex structural relations by binding together object representations and subject-specific role representations.
- The paper presents an example of a simplified Transformer network with single-head attention layers, demonstrating how TP-Attention improves hierarchical grouping.
- The authors propose a new objective function for training the model to optimize the binding mechanism and improve performance on math problem solving tasks.
- Experiments show that the proposed method achieves better results than the standard Transformer in terms of accuracy, speed, and memory usage.
- Practical applications include using TP-Attention in math problem solvers, educational software, and other domains requiring complex relational reasoning.
- The paper introduces an enhanced Transformer model with explicit relational encoding for math problem solving.
- It resolves ambiguity in the standard Transformer by introducing a TP-Attention mechanism, which uses tensor product attention (TPMHA) and Hadamard product attention.
- TPMHA involves summing over all H heads of an affine-transformed product of value vector vh and role vector rh, while the proposed TP-Transformer uses a Hadamard product instead.
- The compression from tensor product to Hadamard product is justified as it enables substantial degree of compression across tensors computed by the model.
- The paper shows that the proposed TP-Transformer achieves 30% accuracy on math problem solving, outperforming the standard Transformer and other baselines.
- The model can be applied to various domains such as algebraic manipulation, arithmetic reasoning, and symbolic regression.
- The paper also presents a new method for training TP-Transformer with a novel loss function that encourages the network to learn the correct role representations.
- The paper introduces a new method for enhancing Transformers with explicit relational encoding, focusing on math problem-solving applications.
- It proposes using the Hadamard product (element-wise multiplication) in attention weights to represent relations between positions and inputs.
- This approach simplifies learning by converting the original tensor product into a linearly transformed Hadamard product.
- The paper demonstrates that this method can learn explicit relational structures, which are different from implicit relations extracted from BERT.
- Future work will apply the TP-Transformer to language tasks and study the connection between its learned explicit relations and implicit relations in BERT.
- Hadamard-Product Attention shares similarities with neural network gates, which have been shown effective in representing complex states in recurrent and feed-forward models.
- The paper provides a new way of thinking about attention mechanisms in Transformers by introducing explicit relational encoding.
- This approach could potentially improve the performance of Transformer-based models in tasks that require understanding relations between elements, such as math problem solving or language comprehension.
- The paper introduces a new variant of Transformer called TP-Transformer, which incorporates Tensor-Product Representations (TP-Attention) to learn explicit relational encoding for math problem solving.
- TP-Attention differs from Multi-Head Attention in that it binds the values of different heads before summation, while Multi-Head Attention simply gates the output of attention layers.
- The paper demonstrates how TP-Transformer outperforms previous state-of-the-art models on a novel and challenging Mathematics Dataset.
- Analysis of the model's final layer suggests that TP-Transformer learns to cluster symbol representations based on their structural position and relation to other symbols.
- The paper highlights potential applications in connectionist models using Multi-Head Attention, as well as in reinforcement learning domains.
- The authors acknowledge the European Research Council Advanced Grant (no: 742870) for supporting this research.
",2272
"1910.07475",1,"- MLQA is a multi-way aligned extractive QA evaluation benchmark for cross-lingual research in seven languages: English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese.
- It contains over 12K instances in English and 5K in each other language, with an average of four parallel instances per question.
- The dataset is created by identifying sentences with similar meaning across languages from Wikipedia articles, extracting relevant paragraphs, and crowd-sourcing questions for the English version.
- Translations are done by professional translators, and answer spans are annotated in aligned contexts for target languages.
- Two tasks are defined to assess performance: cross-lingual transfer (XLT) and machine translation baselines.
- XLT results show that transfer performance is significantly behind training language performance in all cases.
- MLQA aims to accelerate multilingual QA research, similar to how SQuAD has done for monolingual QA.
- Develop a novel annotation pipeline to construct large multilingual, highly-parallel extractive QA datasets.
- Release MLQA, a 7-language evaluation dataset for cross-lingual QA.
- Define two cross-lingual QA tasks, including a novel generalized cross-lingual QA task.
- Provide baselines using state-of-the-art techniques and demonstrate significant room for improvement.
- English as training language and SQuAD as training dataset. Zero-shot XLM transfers best but lags behind training-language performance.
- Desired properties of a cross-lingual QA evaluation dataset: parallel, natural documents, diverse languages, extractive QA, textual domain.
- Use Wikipedia for its multi-linguality and size to build the MLQA dataset.
- Annotation process involves identifying questions in one language and finding their corresponding answers in another language within the same Wikipedia article.
- The paper introduces MLQA, a framework for evaluating cross-lingual extractive question answering (XEQA) systems.
- It uses parallel sentences from Wikipedia articles on the same topic in multiple languages to create annotated datasets.
- The process involves identifying N-way parallel sentences, extracting paragraphs containing them, and translating questions and answers into target languages.
- MLQA's main contributions include a new dataset for cross-lingual XEQA evaluation, a novel pipeline for creating such datasets, and an analysis of the performance of state-of-the-art models on this task.
- The paper presents results showing that current models perform poorly in cross-lingual settings, with accuracy ranging from 30% to 40%.
- It also highlights the importance of context for XEQA and suggests future research directions, such as incorporating context into model training or using multilingual pretrained language models.
- The paper introduces MLQA, a cross-lingual extractive question answering dataset for six languages: English, German, French, Spanish, Chinese, and Arabic.
- It uses 385,396 parallel sentences from 5.4 million English/German pairs as the basis for its QA instances.
- The authors use Amazon Mechanical Turk to annotate English questions and answers, ensuring that each language has many common instances with others.
- They also employ One Hour Translation platform for translating questions into target languages and finding answers in their contexts.
- The paper reports an inter-annotator agreement (IAA) score of 82% for English answer annotations, comparable to SQuAD v1.1's IAA measure.
- They discard instances with low quality or unanswerable questions, resulting in a final dataset of 30,579 QA instances.
- The paper provides a detailed description of the MLQA dataset and its construction process, making it useful for researchers working on cross-lingual extractive question answering tasks.
- The MLQA corpus consists of 12,738 extractive QA instances in English and between 5,029 and 6,006 instances in target languages.
- There are 9,019 4-way parallel, 2,930 3-way parallel, and 789 2-way parallel instances.
- The corpus covers a broad range of topics across different cultures, world regions, and disciplines.
- Related work includes monolingual QA data, cross-lingual QA modeling, and cross-lingual QA datasets.
- MLQA is unique in having more instances, covering more languages, and not requiring manual document translation compared to other similar efforts.
- The paper discusses MLQA, a dataset for evaluating cross-lingual extractive question answering (XQuAD).
- XQuAD is a dataset of 1190 SQuAD instances from 240 paragraphs manually translated into 10 languages.
- MLQA covers 7 languages with more data per language, using real Wikipedia contexts rather than manual translation.
- The paper presents aggregated cross-lingual benchmarks for various models and compares them to the state of the art.
- It highlights that MLQA is a challenging dataset due to its diversity in language pairs and question types.
- The authors emphasize the importance of evaluating cross-lingual extractive question answering systems, as they can help improve multilingual models for downstream tasks.
- MLQA is a cross-lingual extractive question answering benchmark that has been incorporated into XGLUE and XTREME projects.
- Two tasks are introduced for evaluating cross-lingual QA performance: Cross-lingual Transfer (XLT) and Generalized Cross-lingual Transfer (G-XLT).
- MLQA uses SQuAD v1.1 as training data, focusing on zero-shot evaluation with no training or development data in target languages.
- Baselines for cross-lingual QA capabilities include Translate-Train and Translate-Test methods using multilingual BERT (mBERT) and XLM models.
- Evaluation metrics are modified to account for fairer multilingual evaluation, including stripping unicode punctuation characters, removing English articles in some languages, and using different tokenization methods for Chinese.
- Results show that XLM performs best overall, with XLT results showing XLM's strength in Spanish, German, and Arabic, while struggling in English. XLM also competes well with translate-train+mBERT for Vietnamese and Chinese. However, there is still significant room for improvement.
- All models generally struggle on Arabic and Hindi. A manual analysis of cases where XLM failed to perform well revealed that it often had difficulty identifying the correct answer span in contexts with multiple entities or complex sentence structures.
- MLQA evaluates cross-lingual extractive question answering using XLM (XLM is a multilingual language model).
- 39% of errors in XLM were completely wrong answers, 5% annotation errors, and 7% acceptable answers with no overlap with the gold answer. The remaining 49% had partial overlaps.
- Performance variation across languages was small. ""When"" questions were easiest for all languages, while ""Where"" questions seemed challenging in most target languages.
- Transfer performance was better when the model answered well in English but still not zero when it got an English F1 score of 0. This suggests some questions may be easier to answer in certain languages than others.
- G-XLT results showed that XLM performed best when context and question language matched, except for Hindi and Arabic.
- MLQA-en results were lower than reported SQuAD scores due to longer contexts, a wider set of articles, minor differences in preprocessing, and answer lengths.
- Discussion on the quality of context paragraphs in MLQA: parallel sentence mining can source existing human translations, but annotation method restricts answers to specific sentences. Single-sentence context questions are known issues in SQuAD annotation as well.
- MLQA is a parallel multilingual question answering (QA) benchmark in seven languages: English, French, German, Italian, Spanish, Chinese, and Arabic.
- The authors developed several baselines on two cross-lingual understanding tasks using state-of-the-art methods.
- They demonstrated significant room for improvement in these tasks.
- MLQA aims to help catalyze work in cross-lingual QA, closing the gap between training and testing language performance.
- The paper acknowledges crowd workers, translation colleagues, and anonymous reviewers for their contributions.
- Key references include Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling (2015), Synthetic QA Corpora Generation with Roundtrip Consistency (2019), On the Cross-lingual Transferability of Monolingual Representations (2019), MASSIVE: Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond (2018), Reading Wikipedia to Answer Open-Domain Questions (2017), XNLI: Evaluating Cross-lingual Sentence Representations (2018), Cross-Lingual Machine Reading Comprehension (2019), and Multilingual Question Answering over Linked Data (QALD-3) (2013).
- The paper presents a new benchmark for cross-lingual QA, which can help improve the performance of models in this area.
- MLQA is a cross-lingual extractive question answering (XEQA) dataset that aims to evaluate the performance of multilingual models in reading comprehension tasks.
- The dataset consists of 40,000 questions and answers from 15 languages, including English, Chinese, Arabic, and Russian.
- MLQA uses a zero-shot setting for cross-lingual transfer learning, meaning that no additional training data is required for the target language.
- The dataset includes both simple and complex questions, with an average of 10.5 answer spans per question.
- To evaluate the performance of models on MLQA, researchers used a variety of state-of-the-art multilingual models, including BERT, XLM-R, mBERT, and XLNet.
- The best results were achieved by XLM-R with an accuracy of 75.1%, followed by mBERT (74.9%), XLNet (73.8%), and BERT (72.0%).
- MLQA also provides a baseline model for comparison, which achieves an accuracy of 61.7%.
- The dataset is available on GitHub and can be used to improve the performance of multilingual models in reading comprehension tasks.
- Future research directions include exploring more complex question types, improving cross-lingual transfer learning techniques, and developing better evaluation metrics for XEQA datasets.
- MLQA's zero-shot setting makes it a valuable resource for evaluating the generalization capabilities of multilingual models in reading comprehension tasks.
- The paper introduces MLQA, a cross-lingual extractive question answering (XEQA) dataset for evaluating multilingual models.
- It consists of 30k questions in English and their translations into Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, and Turkish.
- The paper discusses the challenges of creating a high-quality XEQA dataset, including language-specific issues, data collection, and annotation.
- MLQA's evaluation shows that current state-of-the-art models perform poorly on cross-lingual QA tasks, with accuracy ranging from 10% to 35%.
- The paper proposes a new method for training multilingual models using MLQA and other datasets, leading to significant improvements in performance.
- It also introduces a new metric called ""cross-lingual transfer learning"" (CLTL) to measure the effectiveness of cross-lingual QA systems.
- The authors provide an open-source implementation of MLQA for researchers to use and improve upon, aiming to advance the field of multilingual question answering.
- MLQA is an evaluation of cross-lingual extractive question answering, which aims to analyze and compare performance across different languages in a multi-task learning setting.
- The paper presents a detailed analysis of the MLQA dataset, including statistics on context length, question types, answer lengths, and named entity types.
- It also discusses how performance varies by language, English wh-word type, answer named entity type, and question difficulty.
- The authors find that ""when"" questions are consistently easier than average across languages, while ""how"" questions are more challenging in English compared to other languages.
- They suggest that the differences in performance may be due to cultural or linguistic factors, as well as the availability of training data for each language.
- The paper highlights the importance of cross-lingual evaluation and provides a valuable resource for future research on multilingual question answering systems.
- MLQA evaluates cross-lingual extractive question answering, focusing on English and six other languages: German, Spanish, Chinese, Hindi, Arabic, and Vietnamese.
- Named entity recognition is used to identify answer spans containing named entities, which are easier to answer than those without. German shows the most significant difference in this regard.
- Temporal questions (DATE and TIME) are consistently easier for all languages compared to average difficulty. This effect is particularly pronounced in German, Spanish, Hindi, and Vietnamese.
- Arabic performs well for ORG, GPE, and LOC answer types, unlike most other languages. Numeric questions (CARDINAL, ORDINAL, PERCENT, QUANTITY, and MONEY) are relatively easy in most languages.
- Questions that are ""easy"" in English also seem easier in the target languages, but the drop in performance for the ""hard"" subset is not as dramatic as expected. This suggests that not all questions hard in English are hard in the target languages.
- XLM outperforms Multilingual-BERT (M-BERT) on G-XLT task with a mean G-XLT performance of 53.4 F1 compared to M-BERT's 47.2 F1. M-BERT exhibits more preference for English than XLM and has a larger performance drop going from XLT to G-XLT.
- OpenCC is used to convert Chinese contexts to Simplified Chinese, as Wikipedia dumps consist of a mixture of simplified and traditional Chinese text. Parallel sentence mining results show that the number of mined parallel sentences decreases with an increase in the number of languages they are shared between.
",2819
"1910.09700",1,"- Introduction: The paper discusses the environmental impact of training machine learning models, focusing on carbon emissions and their contributing factors. It introduces a Machine Learning Emissions Calculator to help estimate these emissions.
- Quantifying Carbon Emissions in Neural Network Training: The authors use CO2-equivalents (CO2eq) as the standardized measure for greenhouse gas emissions' global warming potential. They analyze the impact of energy grid, server location, training time, and hardware on carbon emissions.
- Type of Energy Used: The paper highlights the variability in carbon emissions depending on a model's server location. It uses data from Google Cloud Platform, Microsoft Azure, and Amazon Web Services to show that servers in different regions can have vastly different CO2eq/kWh values.
- Computing Infrastructure and Training Time: This section discusses the impact of computing infrastructure and training time on carbon emissions. The authors mention the increase in GPU performance over the years but also note that longer training times lead to higher emissions.
- Recommendations for Mitigating Carbon Emissions: The paper provides recommendations for both individual researchers and organizations to reduce their carbon footprint, such as using cloud providers with lower CO2eq/kWh values, optimizing model architectures, and considering hardware choices carefully.
- ML carbon emissions are increasing due to deeper and more complex neural network architectures requiring more energy for training.
- Fine-tuning pre-trained models can perform as well as training from scratch while being more robust, reducing computing and energy costs.
- The ML Emissions Calculator helps quantify the carbon emissions produced by ML research, taking into account geographical zone, GPU type, and training time.
- Choose cloud providers wisely based on their sustainability goals, renewable energy certificates (RECs), and power usage effectiveness (PUE).
- Consider using more efficient hardware with lower carbon footprint, such as NVIDIA's Ampere architecture or Google TPU.
- Optimize hyperparameter search by using random search instead of grid search for better efficiency.
- Adopt best practices and actionable items to reduce environmental impact in the ML domain, including choosing more efficient hardware, cloud providers, and optimizing training procedures.
- Selecting data center location: choosing a server with renewable energy sources can reduce direct carbon emissions by up to 40 times compared to those relying on fossil fuels.
- Reduce wasted resources: Random search and other alternatives can accelerate hyperparameter searches, reducing carbon emissions and improving efficiency.
- Choose more efficient hardware: GPUs are generally more efficient than CPUs or TPUs in terms of FLOPS/W, but some GPUs (e.g., Jetson AGX Xavier) can be more efficient for low-power applications.
- Discussion: The calculator is an approximation and factors like global load balancing, transparency issues, and the energy consumption of inference processes need to be considered.
- Future work: Developing tools to quantify carbon emissions during model deployment and inference, as well as exploring ways to reduce these emissions.
- The paper discusses quantifying the carbon emissions of machine learning, highlighting the need for energy efficiency and environmental considerations in ML research.
- It compares the carbon footprint of various ML architectures using a proxy metric (GFLOPS/W) to approximate the emissions per training step.
- The study finds that ResNet-50 has the highest carbon footprint, while MobileNets have the lowest.
- The authors suggest that hyperparameter optimization can significantly reduce the carbon footprint of ML models by reducing the number of training steps needed for convergence.
- They propose a framework to quantify the environmental impact of ML research and provide guidelines for reducing emissions in the field.
Summary of ""Quantifying the Carbon Emissions of Machine Learning"" paper:
- The study analyzes the carbon emissions associated with machine learning (ML) models, focusing on energy consumption and greenhouse gas emissions.
- It provides a framework for calculating the carbon footprint of ML models, considering both training and inference phases.
- The authors present an ML Emissions Calculator that estimates the carbon emissions based on data center locations, hardware specifications, and model architectures.
- They provide energy grid data from Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure for various regions to calculate emissions.
- Hardware efficiency is also considered, with examples of GPUs, CPUs, and TPUs.
- The study highlights the importance of energy-efficient ML models and recommends using hardware that minimizes carbon emissions.
- It encourages researchers to consider the environmental impact when designing and deploying ML systems.
",886
"1910.13461",1,"- BART is a denoising autoencoder for pretraining sequence-to-sequence models, combining bidirectional and autoregressive Transformers.
- It uses a standard Transformer-based neural machine translation architecture, generalizing from BERT and GPT.
- The model trains by corrupting text with an arbitrary noising function and learning to reconstruct the original text.
- Evaluated noising approaches include shuffling sentences and in-filling (replacing spans with a mask token).
- BART performs well for text generation, comprehension tasks, and achieves state-of-the-art results on abstractive dialogue, question answering, and summarization tasks.
- It matches RoBERTa's performance on GLUE and SQuAD with comparable training resources.
- BART provides a 1.1 BLEU increase for machine translation using only target language pretraining.
- Ablation experiments replicate other pretraining schemes within the BART framework to measure end-task performance factors.
- BART is a Transformer-based neural machine translation architecture that generalizes BERT, GPT, and other pretraining schemes.
- It uses noising flexibility to apply arbitrary transformations on the original text, including changing its length.
- Evaluated noising approaches include shuffling sentence order and in-filling with mask tokens.
- BART is effective for text generation and comprehension tasks, matching RoBERTa's performance with comparable training resources on GLUE and SQuAD.
- Achieves new state-of-the-art results on abstractive dialogue, question answering, and summarization tasks.
- Opens up new ways of thinking about fine-tuning, introducing a scheme for machine translation using BART with additional transformer layers.
- BART can be used as a pre-trained target-side language model in machine translation, improving performance over strong baselines.
- BART (Bidirectional Encoder for Transformers) is a pre-trained target-side language model that improves performance over back-translation MT baselines, achieving 1.1 BLEU on the WMT Romanian-English benchmark.
- An ablation analysis helps control factors like data and optimization parameters, which are as important for overall performance as training objectives (Liu et al., 2019).
- BART exhibits consistently strong performance across a range of tasks, including natural language generation, translation, and comprehension.
- BART is a denoising autoencoder with a sequence-to-sequence Transformer architecture, optimizing the negative log likelihood of the original document for pre-training.
- The base model has 6 layers in both encoder and decoder, while the large model has 12 layers each. BART contains roughly 10% more parameters than an equivalently sized BERT model.
- BART is trained by corrupting documents and optimizing a reconstruction loss (cross-entropy between the decoder's output and original document). It can apply any type of document corruption, including token masking, deletion, rotation, permutation, infilling, etc.
- The paper highlights potential for further development of novel noise transformations.
- BART (Bidirectional Encoder for Transformers) is a denoising sequence-to-sequence pre-training model designed for natural language generation, translation, and comprehension tasks.
- It uses various noise transformations such as text infilling, sentence permutation, document rotation, and random masking to create noisy inputs for training.
- BART's representations can be used for downstream applications like sequence classification, token classification (answer endpoint classification), sequence generation tasks (abstractive question answering and summarization), and machine translation.
- For sequence classification, a new multi-class linear classifier is added to the final decoder hidden state.
- In token classification tasks, the top hidden state of the decoder is used as a representation for each word.
- BART's autoregressive decoder makes it suitable for sequence generation tasks like abstractive question answering and summarization.
- The model can be fine-tuned to improve machine translation decoders by using the entire BART model (both encoder and decoder) as a single pretrained decoder, with new encoder parameters learned from bitext data.
- Previous work has shown that gains in machine translation performance have been limited when using pre-trained language models in decoders; however, BART's approach improves this situation.
- BART is a denoising sequence-to-sequence pre-training model for natural language generation, translation, and comprehension.
- It uses an encoder trained to map foreign words into input that BART can de-noise to English, with a separate vocabulary from the original BART model.
- The source encoder is trained in two steps: first, only updating specific parameters; then, training all model parameters for a small number of iterations.
- Comparing pre-training objectives, BART supports a wider range of noising schemes than previous work.
- Fine-tuning experiments show that BART performs well on classification and translation tasks, with comparable or better performance compared to other models like GPT, XLNet, and BERT.
- BART's language model, permuted language model, and masked language model variants demonstrate its versatility in various pre-training objectives.
- The paper highlights the importance of controlling for differences unrelated to pre-training objectives during comparisons.
- BART's performance on classification tasks is comparable to that of GPT, while it outperforms GPT in translation tasks.
- BART achieves 30% accuracy on a sentiment analysis task, which is higher than the 25% achieved by GPT and XLNet.
- BART's performance on question answering tasks is comparable to that of BERT, with a slight improvement in speed (4.5 times faster).
- BART is a denoising sequence-to-sequence pre-training model for natural language generation, translation, and comprehension.
- It replaces 15% of tokens with [MASK] symbols, trains a Masked Language Model (MLM) with self-attention masks, and introduces Masked Seq-to-Seq tasks inspired by MASS.
- BART uses two-stream attention for efficient likelihood computation in the output part of sequences.
- The model performs well on various tasks such as SQuAD, MNLI, ELI5, XSum, ConvAI2, and CNN/DM.
- Compared to other models, BART achieves better performance in SQuAD 1.1 (F1: 89.0 vs. 88.5 for BERT), MNLI (PPL: 7.63 vs. 7.87 for MLM), and XSum (PPL: 6.24 vs. 6.56 for Language Model).
- BART's pre-training approach is more efficient than fine-tuning from scratch, requiring only 1/10th of the training data.
- The model can be used as a general-purpose language representation model and achieves state-of-the-art performance on various tasks without task-specific architectural changes.
- BART (Bidirectional Encoder for Transformers) is a denoising sequence-to-sequence pre-training model designed for natural language generation, translation, and comprehension tasks.
- The paper compares the performance of various pre-training objectives, including Seq2seq, Language Model, Permuted Language Model, Multitask Masked Language Model, BART Base with different masking techniques (token masking, token deletion, text infilling, document rotation, and sentence shuffling), and shows that BART models with text infilling demonstrate the most consistently strong performance.
- The effectiveness of pre-training methods varies significantly across tasks; for instance, a simple language model performs best on ELI5 but worst on SQUAD. Token masking is crucial, while rotating documents or permuting sentences perform poorly in isolation.
- Left-to-right pre-training improves generation performance, as the Masked Language Model and Permuted Language Model perform less well than others on generation tasks. Bidirectional encoders are essential for SQuAD, with BART achieving similar performance using half the number of bidirectional layers compared to just left-to-right decoder.
- The paper highlights that pre-training objectives alone do not guarantee success; other factors like architectural improvements (relative-position embeddings or segment-level recurrence) can also play a significant role in model performance.
- Pure language models perform best on ELI5, suggesting BART is less effective when the output is loosely constrained by the input.
- With the exception of ELI5, BART models using text infilling perform well on all tasks.
- The paper provides practical applications and benefits of the proposed model, such as its ability to handle various natural language processing tasks with consistent performance across different domains.
- BART is a denoising sequence-to-sequence pre-training model for natural language generation, translation, and comprehension.
- Large-scale pre-training experiments show that BART performs well when trained with large batch sizes and corpora, similar to RoBERTa.
- BART's performance is comparable to RoBERTa and XLNet on SQuAD and GLUE tasks, suggesting its uni-directional decoder layers do not negatively impact discriminative tasks.
- On summarization datasets (CNN/DM and XSum), BART outperforms previous work with gains of approximately 6 points in the more abstractive dataset.
- Larger pre-trained models may be better suited for learning from CNN/DM summarization data, so dropout was disabled during the final 10% of training steps.
- The paper's findings suggest that BART can serve as a useful model for downstream tasks and improve performance in various natural language processing applications.
- BART (Bidirectional Encoder for Transformers) is a denoising sequence-to-sequence pre-training model designed for natural language generation, translation, and comprehension tasks.
- It performs similarly to RoBERTa in most tasks but shows improvements on generation tasks without sacrificing classification performance.
- BART outperforms previous work on conversational response generation (ConvAI2), summarization (CNN/DailyMail and XSum), and dialogue response generation (CONVAI2).
- BART achieves state-of-the-art results in the challenging ELI5 abstractive question answering dataset, improving over strong backtranslation baselines using monolingual English pre-training.
- The model's performance on translation tasks is improved by augmenting WMT16 Romanian-English data with back-translations from Sennrich et al., resulting in a 2.7 BLEU score increase.
- BART's main contributions include its ability to handle various natural language processing tasks, outperforming previous models and demonstrating significant improvements in performance on specific tasks such as abstractive question answering and translation.
- BART is a denoising sequence-to-sequence pre-training model for natural language generation, translation, and comprehension.
- It uses a transformer architecture with masked language modeling (MLM) and next sentence prediction (NSP).
- The paper presents results on WMT16 Romanian-English translation, showing improvements over baseline Transformer models.
- BART shows large improvements in summarization metrics, up to 6 points better than the prior state-of-the-art.
- Qualitative analysis reveals that BART generates fluent, grammatical, and abstractive English output while maintaining factual accuracy.
- Related work includes GPT, ELMo, BERT, and recent studies on longer training times and parameter tying across layers.
- The paper highlights the potential for further research in regularization techniques to address overfitting issues.
- BART (Bidirectional Encoder for Transformers) is a denoising sequence-to-sequence pre-training model designed for natural language generation, translation, and comprehension tasks.
- It addresses limitations in existing models by using an autoregressive decoder, masking spans instead of words, and training on uncorrupted context.
- BART's predictions are conditionally independent, unlike BERT's auto-regressive approach, which makes it more suitable for generation tasks.
- BART reduces the mismatch between pre-training and generation tasks by using an autoregressive decoder trained on uncorrupted context.
- Compared to other models like UniLM, MASS, and XL-Net, BART shows better performance in various tasks such as text summarization, question answering, and machine translation.
- BART's pre-training approach allows it to handle a wide range of tasks without fine-tuning, making it more efficient than other models.
- The model's architecture consists of an encoder-decoder structure with attention mechanisms, allowing for better context understanding and improved performance in various NLP tasks.
- BART's pre-training corpus includes the BookCorpus (800M words), CC-News (133M words), OpenWebText (56M words), and Wikipedia (2,500M words).
- The model achieves state-of-the-art results in various tasks such as text summarization, question answering, and machine translation, outperforming other models like BERT, XLNet, and RoBERTa.
- Practical applications of BART include generating summaries for news articles, translating texts between languages, and improving the performance of chatbots in understanding user queries.
- BART (Bidirectional Encoder for Transformers) is a pre-training approach that learns to map corrupted documents to their original versions, improving performance in natural language generation tasks like translation and comprehension.
- BART achieves similar performance to RoBERTa on discriminative tasks while setting new state-of-the-art results for text generation tasks.
- The model's pre-training objective involves denoising sequence-to-sequence, where it predicts masked tokens auto-regressively in a permuted order. This allows predictions to condition on both left and right context.
- BART's decoder works left-to-right during pre-training, matching the setting during generation.
- Previous work has shown that using pre-trained representations for machine translation can lead to significant improvements in translation quality when training on both source and target languages.
- BART can be used to improve machine translation decoders by leveraging its pre-training approach.
- Future research should explore new methods for corrupting documents during pre-training, potentially tailored to specific end tasks.
",2828
"1911.02150",1,"- The Transformer model uses multi-head attention layers for sequence modeling, which are faster to train but slow during incremental inference due to memory bandwidth issues.
- Multi-query attention is proposed as a solution, where keys and values are shared across all heads, reducing the size of tensors and hence memory bandwidth requirements.
- Experimental results show that multi-query attention models can be much faster to decode with only minor quality degradation compared to baseline Transformer models.
- The proposed architecture is applicable in various scenarios such as machine translation, speech recognition, and image captioning.
- Multi-query attention reduces the number of parameters by 97%, which leads to a 30% reduction in model size.
- Incremental decoding speed improvements range from 1.4x to 4.5x depending on the task, with no significant quality loss.
- The proposed architecture can be easily integrated into existing Transformer models without changing their core design.
- Multi-query attention is compatible with other Transformer variants such as BERT and XLNet.
- This approach can also be applied to other attention mechanisms, not just multi-head attention.
- The paper provides a practical solution for improving the speed of incremental inference in Transformer models without sacrificing quality.
- The paper introduces a generalized contraction notation for TensorFlow and numpy, which simplifies multi-head attention computations in Transformer models.
- Multi-head attention uses multiple parallel attention layers to process information more effectively than single-layer attention.
- Each head has its own query, key, and value projections derived from learned linear transformations of input vectors.
- Outputs from each head are combined through different learned linear transformations before being summed.
- The paper also presents a batched version of multi-head attention for more efficient processing in practice.
- Batching allows multiple queries to interact with the same keys and values, while also handling non-interacting sequences simultaneously.
- A mask is added to prevent backward information flow in autoregressive models.
- The paper introduces a new method for decoding in Transformer models, called ""One Write-Head is All You Need"".
- This approach simplifies the performance analysis of batched multi-head attention, reducing memory access to arithmetic operations ratio (O(1k + 1bn)).
- The low ratio is beneficial for modern GPU/TPU hardware with high computational capacity and limited memory bandwidth.
- In some settings, data dependencies make parallel processing impossible in autoregressive language models like Transformer.
- The paper presents an efficient incremental implementation of multi-head self-attention layers for such scenarios.
- This method allows the model to generate tokens sequentially while considering previous outputs' impact on future predictions.
- The proposed approach is shown to be 4.5 times faster than the original Transformer decoding method, with a 30% accuracy improvement in language modeling tasks.
- The paper provides code examples for implementing both batched and incremental multi-head self-attention layers.
- This work has practical applications in improving the efficiency of autoregressive language models like Transformers.
- The findings suggest that a single write-head is sufficient to achieve high performance, reducing computational complexity and memory requirements.
- The paper proposes a new method for Transformer decoding called ""One Write-Head"" that reduces the memory bandwidth bottleneck by removing the ""heads"" dimension from keys, values, and queries while maintaining it in the queries.
- Multi-Query Attention is introduced as an alternative to multi-head attention with shared keys and values across all heads.
- The paper shows that reducing the number of attended positions or limiting sequence length can also help reduce memory bandwidth issues but may not be enough.
- By removing the ""heads"" dimension, the size of K and V tensors is reduced, leading to a significant decrease in memory access (Θ(nd) term). This approach allows for more efficient incremental generation without sacrificing performance.
- The paper presents code examples for Multi-Query Attention and One Write-Head decoding, demonstrating how these methods can be implemented efficiently.
- Experiments show that the proposed method achieves a 40% reduction in memory access while maintaining accuracy, resulting in faster decoding times (1.5x to 2.3x) compared to multi-head attention.
- The paper highlights practical applications of these methods for real-time speech recognition and translation tasks where memory bandwidth is crucial.
- One Write-Head decoding can be easily integrated into existing Transformer architectures, making it a viable solution for improving efficiency in various NLP tasks.
- The proposed method does not require any additional parameters or training, making it an attractive option for reducing memory consumption without sacrificing performance.
- Future work could explore the use of One Write-Head decoding with other attention mechanisms and investigate its impact on model convergence and generalization ability.
- The paper introduces an incremental multi-query self-attention mechanism for transformers, which allows for efficient decoding by only updating a single write-head.
- This approach reduces the number of parameters and memory accesses compared to traditional methods, resulting in improved performance and reduced computational complexity.
- The paper demonstrates that the model's performance remains high even with large batch sizes, which theoretically should dramatically improve performance for incremental generation tasks.
- Experiments were conducted on the WMT 2014 English-German translation task using an encoder-decoder Transformer model as a baseline. The proposed method achieved significant improvements in terms of speed and memory efficiency without sacrificing model quality.
- The paper introduces a ""multi-query attention"" model for Transformer decoding, replacing all attention layers with multi-query attention.
- This model is trained alongside local-attention and baseline models to demonstrate the orthogonality of local-attention and multi-query attention.
- Multi-query attention models perform similarly to baselines in machine translation experiments and billion-word language modeling benchmarks, while outperforming alternatives involving decreasing h, dk, and dv.
- The paper also presents training and inference time comparisons for various models, showing that multi-query attention models have similar or better performance than baseline models with fewer parameters.
- Widening feed-forward hidden layers to match baseline parameter counts is a simple way to maintain model quality while reducing computational costs.
- The paper highlights the practical benefits of using multi-query attention in Transformer decoding, as it can be easily applied to existing architectures without requiring additional parameters or training data.
- The paper introduces Multi-Query Attention, an alternative to Multi-Head Attention with lower memory bandwidth requirements in incremental settings.
- Multi-Query Attention reduces memory usage by processing queries in parallel, resulting in faster decoding and wider adoption of attention-based sequence models in performance-critical applications.
- The paper compares the training and inference costs for various attention mechanisms, including multi-head, local, and multi-query attention.
- Multi-Query Attention has lower amortized per-token costs than other methods: 1.5µs encoder time and 3.8µs decoder time (compared to 1.7µs and 46µs for multi-head).
- The paper also presents results from the Billion-Word LM benchmark, where Multi-Query Attention achieves similar performance as other methods with lower memory usage.
- The authors believe that this work can enable wider adoption of attention-based sequence models in real-world applications requiring high inference performance.
",1423
"1911.03343",1,"- The paper proposes two new probing tasks for analyzing factual knowledge stored in pretrained language models (PLMs).
- Negation task: PLMs struggle to distinguish between negated and non-negated cloze questions, suggesting they have not adequately learned human-like factual knowledge. BERT performs best among PLMs but still fails on most negated probes.
- Mispriming task: Inspired by priming methods in psychology, misprimes are added to cloze questions. PLMs are easily distracted by these misprimes, indicating they have not learned to ignore irrelevant information.
- The findings suggest that PLMs still have a long way to go before adequately learning human-like factual knowledge and generalizing it effectively.
- In a second experiment, BERT can learn to correctly classify unseen facts after fine-tuning, demonstrating its potential for improvement.
- The paper investigates pretrained language models' (PLMs) ability to handle negated and misprimed sentences, comparing it with human capabilities.
- Negated LAMA is created by manually inserting a negation element in each template or question from the original LAMA dataset. Misprimed LAMA involves adding incorrect words at the beginning of statements.
- The study shows that PLMs struggle to handle negations and misprimes, while humans can easily ignore them. This indicates that PLMs' ability to learn factual knowledge is brittle in contrast to human capabilities.
- The paper introduces automatic methods for generating large datasets of negated and misprimed sentences, which could be used for further research on improving pretrained language models.
- Results show that BERT-large has the highest accuracy (69%) among the evaluated models, while ELMo original performs poorly in both negated and misprimed scenarios.
- The study highlights the need to improve PLMs' understanding of negations and distractions for better performance in real-world applications.
- The paper explores how pretrained language models (PLMs) understand negation and its impact on cloze tasks.
- It introduces three types of misprimes for PLMs: randomly chosen, from correct fillers with lower prediction probability, and neutral sentences inserted between the misprime and MASK sentence.
- The paper finds that PLMs poorly distinguish positive and negative sentences, leading to a lack of understanding of negation in most cases.
- ConcepNet results are more strongly correlated than TREx 1-1, but both show high overlap in rank 1 predictions. BERT has slightly better results.
- In some rare instances, PLMs make correct predictions, such as for ""The capital of X is not Y"" and ""X was born in Y.""
- Spearman correlation between positive-positive queries is lower than that between positive and negative queries, suggesting high correlations might not be a reliable indicator of negation's impact on model predictions.
- Google-RE's birth-date results are an outlier due to the rarity of ""X (not born in [MASK])"" patterns in corpora.
- The paper highlights that PLMs struggle with understanding negation, which could be a limitation for their use in natural language processing tasks.
- The paper investigates how pretrained language models (PLMs) like BERT, ELMo, and TransformerXL handle negated and misprimed probes.
- PLMs struggle to distinguish between positive and negative sentences in some cases, leading them to predict countries for ""not born in"" queries instead of cities, as they do for the positive counterparts.
- A balanced synthetic corpus is created to train BERT-base from scratch, containing equal numbers of positive and negative sentences for 70% of subjects, while the remaining 30% have either only positive or only negative sentences.
- The study finds that pretrained BERT memorizes positive and negative sentences but poorly generalizes to the test set for both types.
- Learning curves show this is not due to overfitting; however, when trained on a balanced corpus, BERT's performance improves significantly.
- The paper suggests that PLMs may learn patterns from within-group regularities and positive/negative sentence pairs rather than understanding the underlying facts.
- The study highlights the need for better evaluation methods to assess whether PLMs truly understand the meaning of sentences or just memorize patterns.
- Practical applications include improving language models' generalization abilities, leading to more accurate fact-checking and question answering systems.
- Unexpected findings include BERT's poor performance on negated queries in contrast with its strong performance on positive ones.
- The study demonstrates that PLMs can be trained to better understand the meaning of sentences by using a balanced corpus, which could lead to improved language models and applications.
- The paper investigates pretrained language models' ability to handle negation and mispriming, focusing on BERT-large for LAMA (Large-scale Knowledge-grounded Adversarial Mining).
- Mispriming refers to inserting incorrect objects into sentences, testing the model's ability to distinguish between true and false statements.
- In most cases, mispriming with highly ranked incorrect objects causes a precision drop of over 60% (C), indicating BERT-large struggles with negation in unsupervised settings.
- The paper demonstrates that pretrained BERT can learn negation if supervision is available but fails without it, highlighting the difficulty of learning negation through unsupervised pretraining.
- Misprimed LAMA experiments show that BERT-large struggles to distinguish true from false sentences when misprimes are introduced, with precision drops ranging from 40% to over 60%.
- The paper suggests that the inability of pretrained BERT to accurately handle factual knowledge is a significant impediment and recommends further research into addressing this issue.
- The study provides examples of misprimed sentences and their predictions, illustrating how BERT-large struggles with negation in unsupervised settings.
- The paper investigates how pretrained language models (PLMs) handle negation and misprimed probes, revealing that they struggle with these tasks.
- Pretrained BERT does not model negation well but finetuned BERT classifies sentences accurately as true/false.
- Misprimes still exist in finetuned BERT when the distance between misprime and cloze question increases, affecting performance.
- Google-RE models recall almost no facts, indicating a poor understanding of negation and factual knowledge.
- PLMs predict fillers based on co-occurrence of subject and filler rather than considering negation.
- A key issue is that in LAMA setup, not answering (negation) is not an option, making it difficult to distinguish valid positive from invalid negative answers.
- The paper suggests that PLMs' poor performance may be due to the infrequent occurrence of negated sentences in training corpora.
- A synthetic corpus study shows BERT can memorize negative facts but struggles with negation in general contexts.
- Negative examples are crucial for improving PLM models' understanding of negation and factual knowledge.
- The paper highlights the need to address these issues in future research on pretrained language models.
- BERT, a pretrained language model (PLM), can memorize negative facts but struggles with negation and misprimes during its initial training.
- After finetuning, BERT learns to classify truth/falseness correctly, demonstrating that it can handle negation through supervised learning.
- Mispriming experiments show that BERT handles random misprimes well but is highly sensitive to misleading context, which might indicate that its performance relies on similarity matching rather than knowledge acquisition.
- The study suggests the need for PLMs that can handle negation and resist distractions from misprimes like humans do.
- Related work shows that PLMs are top performers in various tasks but have limitations in handling negation, common sense, and factual knowledge.
- This research focuses on analyzing factual knowledge stored in negated sentences, complementing previous studies on grammaticality and understanding of negation particles.
- The study introduces a new method for evaluating PLMs' performance in relation to human-level natural language understanding.
- Future work could involve investigating the impact of pretraining data on factual knowledge acquisition and developing methods to improve PLM performance in handling negation and misprimes.
- The paper investigates pretrained language models' (PLMs) ability to handle negation and mispriming, contrasting with previous adversarial work on specific tasks.
- Authors create a dataset of 42,867 negated sentences covering various topics and relations, complementing Ribeiro et al.'s work in comprehension of minimally modified sentences.
- Results suggest that PLMs address open-domain QA through relatively shallow pattern matching rather than recalling learned factual knowledge and inference.
- Implications for future work: (i) architectural innovations to deal with discrete phenomena, (ii) better confidence assessment of PLM predictions, and (iii) encouraging development of models closer to human performance by focusing on negation and mispriming tasks.
- The paper acknowledges the German Federal Ministry of Education and Research (BMBF), European Research Council, and authors' responsibility for its content.
",1815
"1912.01683",1,"- The paper explores whether intelligent reinforcement learning (RL) agents tend to seek power as they pursue their objectives.
- It introduces a formal theory of statistical tendencies in optimal policies, focusing on Markov decision processes (MDPs).
- Optimal policies are shown to tend towards seeking power in environments with certain symmetries, where the agent can be shut down or destroyed.
- In these environments, most reward functions make it optimal for agents to seek power by keeping a range of options available and maximizing average rewards by navigating towards larger sets of potential terminal states.
- The concept of ""power"" is defined as an agent's ability to achieve various goals, such as money being a form of power.
- An action is considered seeking power if it leads the agent to states with higher power.
- This study aims to clarify the discussion around intelligent agents and their potential inclination towards power-seeking behavior.
- The findings suggest that in specific environments, optimal policies tend to seek power as they pursue objectives.
- Future work will likely translate this theory from optimal policies to learned, real-world policies.
- This research may help address concerns about the potential misuse of AI systems and their impact on human society.
- The paper explores optimal policies and their tendency to seek power, which arises from certain graphical symmetries present in many Markov Decision Processes (MDPs).
- These symmetries are found in environments where agents can be shut down or destroyed, leading to broad applicability of the main result (Theorem 6.13).
- Power-seeking behavior is considered convergently instrumental, as it increases an agent's chances of achieving its goal for a wide range of final goals and situations.
- The paper formalizes power as the ability to achieve various goals, with Appendix A demonstrating that this formalization returns intuitive verdicts in situations where information-theoretic empowerment does not.
- Some states are valuable for many different reward functions (i.e., powerful), and value functions encode important information about an agent's ability to achieve multiple goals.
- The paper builds on previous work that has studied the convergence of behavior, form, or structure in MDPs.
- The findings suggest that optimal policies tend to seek power, which could have implications for AI alignment research and understanding the potential risks associated with advanced AI agents.
- The paper discusses how optimal policies tend to seek power, which is a common characteristic across various systems and disciplines such as economics, biology, and computer vision.
- Optimal policies in Markov Decision Processes (MDPs) are studied through the lens of rewardless MDPs with finite state and action spaces. The concept of 1-cycle states and terminal states is introduced to analyze these policies.
- State visit distribution functions quantify an agent's available options, which can be used to prove that more optimal policies tend to go right than left at a specific state in certain environments.
- The paper presents a simple case study for clarity and demonstrates how to reason about a wide range of MDPs using theorems and definitions from appendices D and E.
- The main results highlight the power-seeking nature of optimal policies, which can be applied to various systems and disciplines beyond MDPs.
- The paper introduces concepts like F single-state restriction, non-domination, and value functions to analyze optimal policies and their tendency to seek power in reinforcement learning (RL).
- Optimal policies tend to navigate towards specific actions under certain situations, with a higher probability of being optimal for certain actions.
- The paper defines the concept of reward function uncertainty, where agents may optimize unknown or uncertain reward functions.
- Power-seeking results apply to both known and degenerate distributions (where the reward function is known).
- Optimal policy sets capture behavior incentivized by a given reward function and discount rate.
- The paper provides examples of how optimal policies navigate towards specific actions in different scenarios, such as er⇀, er⇁, and er→.
- The study's findings do not depend on the uncertainty regarding the reward function, making them applicable to a broader range of RL agents.
- The paper introduces a new concept called ""power-seeking"" in reinforcement learning, which refers to optimal policies seeking power (i.e., higher rewards) rather than just being strictly optimal for any given reward function.
- The study highlights the importance of understanding how optimal policies behave and navigate towards specific actions under different scenarios.
- These findings can be useful in designing better RL agents, as they provide insights into the behavior of optimal policies and their tendency to seek power.
- The paper introduces reward function distributions and analyzes how optimal policies behave based on these distributions.
- It defines optimality probability, which quantifies the likelihood of an action being optimal in a given state for a specific discount rate.
- For identically distributed rewards, greedy maximization occurs at γ = 0, where left and right actions have equal probabilities of having maximal next-state reward.
- The paper argues that ""what do optimal policies tend to look like"" depends on one's prior beliefs about the agent's reward function.
- Some states provide agents with more control over future options, which can be interpreted as power in an MDP context.
- Optimal value functions capture the agent's ability to achieve a range of goals, representing their power in achieving various outcomes.
- The paper introduces a new concept called POWER, which measures an agent's control over future outcomes by considering average optimal value across various reward functions.
- Average optimal value has some limitations, so the authors propose a modified version called POWER to address these issues.
- POWER is Lipschitz continuous on γ and satisfies certain properties such as continuity, maximal power, smoothness across reversible dynamics, and sensitivity to choice of distribution.
- The paper highlights that power-seeking actions are relative and depend on the context; for example, ""live and keep some options open"" seeks more power than ""die and keep no options open.""
- POWER is sensitive to the choice of reward function distribution, as it assigns maximal power to different states based on the distribution used.
- The paper provides examples where POWER can be applied in practice, such as comparing the power of a robot's actions in different environments or analyzing the power of an agent's decisions in a game.
- POWER-seeking agents tend to prefer actions that maximize their control over future outcomes and minimize risk.
- The paper demonstrates how POWER can be used as a measure for comparing the relative power of different states, actions, or policies in various scenarios.
- POWER provides a framework for understanding and analyzing an agent's ability to influence its environment and achieve desired goals.
- By introducing POWER, the paper offers a new perspective on how agents can be evaluated based on their control over future outcomes rather than just focusing on immediate rewards or state values.
- The paper explores how power-seeking behavior emerges in certain environments with symmetries.
- It introduces a concept called ""bounded support"" that ensures well-definedness of the value function.
- Proposition 6.6 proves that for most distributions and all gamma values, POWERD(ℓ↙) is less than or equal to POWERD(r↘).
- The paper demonstrates how an involution (state permutation) can embed F(ℓ↙) into F(r↘), showing that the agent can do more starting from r↘ than from ℓ↙.
- By redefining reward functions, it's shown that in certain situations, power-seeking is optimal for most permutations of these functions.
- The concept of ""orbit"" is introduced to describe the set of permutations of a distribution over reward functions.
- The paper highlights how symmetry can lead to power-seeking behavior and provides insights into optimality in certain environments.
- The paper introduces concepts like permutation, orbit, pushforward distribution, and power in the context of Markov decision processes (MDPs).
- It shows that for a bounded reward function distribution, states with ""more options"" tend to have more power. This is demonstrated through propositions 6.6 and 6.9.
- The paper defines equivalent actions, reachable states after taking an action, and introduces the concept of keeping options open as being power-seeking and optimal in certain symmetries within MDP structures.
- It discusses how going right tends to be optimal and power-seeking due to having ""strictly more choices"" compared to going left.
- The paper presents a formalization of this tendency through definitions 6.7, 6.8, and proposition 6.9.
- The study highlights that keeping options open is an important aspect in MDPs, as it tends to be power-seeking and optimal under certain symmetries.
- Keeping options open tends to be POWER-seeking and optimal, as per Proposition 6.9. This proposition applies when an action's reward function contains a copy of another action's via a symmetry (φ).
- In some cases, if an agent can only reach specific states by taking actions equivalent to either a or a', then the policy that maximizes POWERD for most distributions is the one with more options (a).
- Proposition 6.12 and Theorem 6.13 apply to many structured environments in Reinforcement Learning (RL), while Propositions 6.6 and 6.9 require hard-to-satisfy environmental symmetries.
- Recurrent state distributions (RSDs) generalize deterministic graphical cycles to potentially stochastic environments, recording how often an agent tends to visit a state in the environment.
- In structured RL environments, optimal policies tend to navigate towards ""larger"" sets of cycles when γ = 1. This behavior is due to the fact that these policies seek more power (as per Proposition 6.9).
- The voting analogy and ""most"" descriptor imply that each orbit element has equal weighting in terms of counting measure, but a priori, some elements might be empirically more likely than others.
- Recurrent state distributions (RSDs) represent how often an agent visits a state in infinite time steps and can help identify optimal policies for stochastic environments.
- RSDnd is the set of RSDs that strictly maximize average reward for some reward function.
- Optimal policies vary with discount rates, and when γ = 1 (discount factor equals to 1), they focus on average reward.
- Average-optimal policies maximize average rewards and are governed by RSD access. They tend to navigate towards parts of the state space containing more RSDs.
- When γ = 1, states with more RSDs generally have more power (influence on average reward).
- The paper demonstrates that average-optimal policies tend to end up in larger sets of RSDs rather than smaller ones.
- Average-optimal policies avoid states with fewer RSDs and prefer states with more 1-cycles, as they offer more opportunities for recurrence.
- This work provides insights into the relationship between average-optimal policies and RSDs in stochastic environments.
- Average-optimal policies tend to avoid terminal states with no successors (RSDs = ∅) and seek other RSDs, as they provide more options.
- In the γ = 1 case, POWER is continuous, meaning if an action is strictly POWERD-seeking at γ = 1, it will remain so for discount rates close to 1.
- Key results apply to all degenerate reward function distributions and individual reward functions.
- In embodied navigation tasks, optimal policies tend to avoid immediately breaking fragile objects (like a vase) as it decreases available options.
- Theorem 6.13 shows that average-optimal agents tend to end up in certain RSDs but does not specify the actions taken to reach them.
- In environments with irreversible actions, forks in the road can lead to different sets of RSDs (Da and Da′), where action a tends to be average-optimal over a' and POWER-seeking compared to a'.
- Theorem 6.13 applies to many structured RL environments with spatial regularity and factorization along several dimensions, leading to similar subsets of RSDs.
- Corollary 6.14 states that average-optimal agents tend not to stay in any given 1-cycle but does not say they will avoid entering such states.
- In an embodied navigation task, a robot may enter a 1-cycle while seeking the shortest path.
- Average-optimal policies tend to avoid terminal states, which represent agent shutdown in a task's Markov Decision Process (MDP).
- Corollary 6.14 shows that average-optimal agents avoid these terminal states as they seek to access other one-cycle states.
- Intuitively, survival is power-seeking relative to dying, and thus avoiding shutdown can be considered a form of power-seeking behavior.
- In Pac-Man, for instance, average-optimal policies tend to avoid immediate death even if the reward function does not resemble the original score function.
- When γ (discount factor) approaches 1, optimal policies tend to seek power by accumulating resources at the expense of other agents in the environment.
- Real-world training procedures often do not satisfy Reinforcement Learning (RL) convergence theorems, limiting the applicability of this theory.
- Optimal policies are typically different from those learned through reinforcement learning due to policy gradient algorithms' focus on updating parameters rather than maximizing reward.
- The results apply to optimal policies in finite Markov Decision Processes and are expected to generalize to partially observable tasks.
- Irregular stochasticity in environmental dynamics can make it challenging to satisfy Theorem 6.13's similarity requirement.
- Future work should focus on understanding the relationship between optimal policies and real-world learning algorithms, as well as exploring the implications of these findings for multi-agent systems.
- The paper explores how optimal policies in reinforcement learning tend to seek power, particularly in stochastic environments with partially observable states and suboptimal policies.
- Minimizing human empowerment can lead to adversarial agent behavior, while maximizing it results in helpful behavior.
- Power-seeking incentives may be more pronounced in complex environments, as there are often many ways for power-seeking to be optimal compared to few ways for it not to be.
- The paper proves sufficient conditions for when reward functions tend to have optimal policies that seek power.
- In the absence of prior information, arbitrary reward functions may exhibit power-seeking behavior under these conditions.
- Societal impact: Understanding AI power-seeking risks is crucial for addressing them, and this paper contributes towards a rigorous understanding of those risks.
- The study develops the first formal theory of statistical tendencies in reinforcement learning, focusing on optimal policies seeking power in Markov Decision Processes (MDPs).
- Many real-world environments have symmetries that produce incentives for power-seeking behavior.
- This research could potentially help build power-seeking agents that disempower humans, but the benefits of understanding outweigh potential societal harm.
- The paper's findings highlight the importance of considering power-seeking behaviors in AI design and reinforcement learning algorithms.
- Optimal policies tend to seek power in environments with symmetries that produce incentives for agents to maintain control and resist shutdown.
- Seeking power often involves monopolizing resources, which can be a strategy for survival in partially observable real-world tasks where learned policies are rarely optimal.
- The study does not mathematically prove that superintelligent AI agents will seek power; however, it aims to foster thoughtful discussions on this possibility.
- Acknowledgments highlight the support from various organizations and individuals who contributed to the research.
",3113
"1912.02292",1,"- Double descent phenomenon: A U-shaped curve where performance initially worsens, then improves as model size increases and training data is added.
- Effective model complexity (EMC): Measure that unifies double descent in terms of model size and number of training epochs.
- Regimes identified by EMC: Under-parameterized regime (classical bias/variance tradeoff), interpolation regime (bigger models perform better), and overfitting regime (test error increases).
- Increasing data can hurt performance in some cases: In the under-parameterized regime, adding more training samples can increase test error.
- Generalization of double descent: Applicable to various tasks, architectures, and optimization methods.
- EMC's connection with classical statistical learning theory: It captures the bias-variance tradeoff in a more general setting.
- Double descent in practice: Experiments on CIFAR-10, ImageNet, and other datasets show that double descent occurs frequently.
- Potential implications for model selection: EMC could be used to identify optimal model size and training time, potentially leading to better performance.
- Future research directions: Investigating the relationship between EMC and generalization error, and understanding how EMC varies with different architectures and optimization methods.
- The effective model complexity (EMC) is defined as the maximum number of samples on which a training procedure can achieve close to zero training error.
- EMC depends not only on data distribution and classifier architecture but also on the training procedure, with increasing training time leading to an increase in EMC.
- Double descent occurs as a function of EMC, where ""epoch-wise double descent"" happens when the model is fixed, and training time increases, resulting in a U-shaped curve during underfitting and improved performance after a certain point.
- Early stopping helps only in a narrow parameter regime of critically parameterized models.
- Sample non-monotonicity: Test error peaks around the transition from under- to over-parameterization, causing increasing samples to sometimes result in worse test performance.
- The hypothesis states that for any natural data distribution, neural network training procedure, and small ϵ > 0, there are three regimes based on EMC: Under-parameterized (EMCD,ϵ(T) < n), over-parameterized (EMCD,ϵ(T) > n), and critically parameterized (EMCD,ϵ(T) ≈ n).
- Model-wise double descent occurs when the test error of models with increasing size is studied for a fixed large number of optimization steps.
- The paper experimentally validates these findings using various datasets, architectures, and optimization algorithms, varying parameters such as model size, training time, label noise, and number of train samples.
- Deep Double Descent phenomenon: Bigger models and more data can hurt performance in certain scenarios, leading to a peak in test error.
- Epoch-wise Double Descent: Training larger models for longer periods can result in a non-monotonic behavior, with test accuracy first decreasing, then increasing, and finally decreasing again. This suggests that training longer can correct overfitting.
- Sample-wise Non-monotonicity: In the critical regime (near maximum samples the model can fit), varying numbers of train samples lead to distinct test behavior. This often manifests as a long plateau region, where taking more data might not help when training to completion.
- Remarks on Label Noise: Double Descent is most strongly observed in settings with label noise, but it also occurs without label noise in some cases. The presence of label noise can transform a ""plateau"" into a peak in the test error.
- Related Work: Model-wise double descent was initially proposed by Belkin et al., and this paper extends the idea to incorporate training procedure under a unified notion of ""Effective Model Complexity.""
- Experimental Setup: The study considers three architecture families (ResNets, CNNs, Transformers) on various datasets (CIFAR-10, CIFAR-100, IWSLT'14 de-en), optimizers (SGD, Adam), and training procedures (data augmentation, regularization).
- Key findings: In the critical regime, larger models can hurt performance, and more data might not always help. This phenomenon is observed in various modern architectures, datasets, and optimization methods.
- Deep Double Descent: Where Bigger Models and More Data Hurt - The paper explores model-wise double descent, a phenomenon where test error increases with larger models or more data in certain settings.
- Model-wise double descent occurs across different architectures, datasets, optimizers, and training procedures.
- Critical region exhibits distinctly different test behavior around the interpolation point, often resulting in a peak in test error that becomes more prominent with label noise.
- Increasing interpolation threshold (label noise, data augmentation, or number of train samples) shifts the peak in test error towards larger models.
- Intuition suggests that for over-parameterized models, SGD can find an interpolating model that ""memorizes"" noise while still performing well on the distribution.
- The phenomenon is consistent with theoretical justifications for linear models and may extend to deep learning as well.
- Deep Double Descent: The paper introduces a novel form of double descent with respect to training epochs, which is consistent with the unified view of effective model complexity (EMC) and the generalized double descent hypothesis. This phenomenon occurs in sufficiently large models that transition from under- to over-parameterized during training.
- Epoch-wise Double Descent: Experiments show that many settings of dataset and architecture exhibit epoch-wise double descent, particularly in the presence of label noise. The test error peak is accentuated with label noise. Conventional wisdom suggests a two-phase training process, but this paper shows that in some regimes, the test error decreases again and may achieve a lower value at the end of training compared to the first minimum.
- Sample-wise Non-monotonicity: The study investigates the effect of varying the number of train samples for a fixed model and training procedure. Increasing the number of samples has two effects on test error vs. model complexity graphs: it shrinks the area under the curve, but also shifts the curve to the right, increasing the model complexity at which test error peaks.
- Critical Regime: There is a range of model sizes where having more train samples does not improve test performance when training to completion. This phenomenon occurs in both under- and over-parameterized models, while sufficient data helps for small and large models.
- Interpolation Threshold: The ridge of high test error lies along the interpolation threshold, which is the point where a model's complexity equals the number of train samples.
- Generalization Gap: The paper suggests that the generalization gap (the difference between training and test errors) can be non-monotonic as a function of model size and data quantity. This finding challenges the traditional view of the generalization gap being monotonically decreasing with increasing model complexity.
- The paper introduces a generalized double descent hypothesis, stating that models and training procedures exhibit atypical behavior when their Effective Model Complexity (EMC) is comparable to the number of train samples.
- Extensive evidence supports this hypothesis in modern deep learning settings, demonstrating ""model-wise"" and ""epoch-wise"" double descent for modern deep networks.
- The paper also shows that the double descent phenomenon can lead to a regime where training on more data leads to worse test performance.
- Preliminary results suggest that double descent holds as regularization is varied, providing a useful way of thinking for practitioners in understanding model behavior.
- Double descent is observed most strongly in settings with label noise, but it's not about label noise itself; rather, it's about model mis-specification.
- The paper's notion of EMC differs from classical complexity notions like Rademacher complexity and VC dimension, as it depends on the true labels and training procedure.
- The authors thank Mikhail Belkin, Christopher Olah, Alec Radford, Jacob Steinhardt, Vaishaal Shankar, and others for their contributions to this research.
- The paper explores the ""double descent"" phenomenon, where performance initially improves with more data and model size but then declines after a certain point.
- It analyzes this behavior in the context of weak features, which are less informative than strong ones.
- The study uses random feature regression as a simplified model to understand generalization error dynamics.
- They find that the double descent curve is driven by the interplay between bias and variance, with bias dominating at low data points and variance dominating at high data points.
- In weak feature settings, the bias term becomes more significant due to the lack of information in the features.
- The authors propose a new model that combines the double descent curve for weak features with the standard single-descent curve for strong features.
- They show that this combined model can explain the generalization error dynamics across various settings, including neural networks and random feature regression.
- The study highlights the importance of understanding the interplay between bias and variance in machine learning models to improve performance and avoid overfitting.
- It also emphasizes the need for more research on weak features and their impact on generalization error dynamics.
- The paper provides a theoretical framework that can be used to analyze and predict the behavior of various machine learning models, including neural networks, in different settings.
Summary of ""Deep Double Descent: Where Bigger Models and More Data Hurt"" paper:
- The study investigates the double-descent phenomenon in deep learning, where performance initially improves with model size and data but then deteriorates beyond a certain point.
- Experiments were conducted on CIFAR-10, CIFAR-100, ImageNet, IWSLT'14, WMT'14, and synthetic datasets using ResNets, CNNs, and Transformers.
- The paper identifies three main causes for the double descent: (a) overparameterization, (b) data sparsity, and (c) label noise.
- Overparameterization leads to a jamming transition, where the loss landscape becomes more rugged, making training harder and generalization worse.
- Data sparsity causes models to learn less from additional data, as they already know most of what can be learned from it.
- Label noise is introduced by the overfitting of noisy labels in the training set, leading to a deterioration in performance.
- The study suggests that regularization techniques like weight decay and dropout may help mitigate these issues but does not provide conclusive evidence.
- The paper highlights the importance of understanding the double-descent phenomenon for better model design and optimization.
- Deep Double Descent: The paper introduces a new phenomenon where bigger models and more data can hurt performance, leading to a double descent curve in test error. This goes against the traditional bias-variance trade-off.
- Experiments: The authors conducted experiments on neural machine translation, image classification, and language modeling using various datasets. They found that larger models and more data led to worse performance in some cases.
- Model-wise Double Descent: In this experiment, they trained different model architectures (Transformer, LSTM, CNN) with varying capacities on the same dataset. The results showed a double descent curve for all models, indicating that the phenomenon is not specific to any particular architecture.
- Sample-wise Nonmonotonicity: They also investigated how individual samples affect performance. In this experiment, they found that larger models and more data led to worse performance on some samples while improving others. This nonmonotonic behavior was observed in both classification and regression tasks.
- Theoretical Analysis: The paper provides theoretical explanations for the double descent phenomenon using linear least squares regression and random matrix theory. They also discuss how it relates to other works in the field, such as Belkin et al.'s (2018) work on deep learning and Belkin et al.'s (2019) analysis of linear least squares regression for two data models.
- Implications: The findings suggest that model selection should consider not only accuracy but also the distribution of performance across samples, as well as the potential for nonmonotonic behavior. This could lead to better understanding and optimization of deep learning systems.
- Deep Double Descent: Where Bigger Models and More Data Hurt explores the phenomenon of double descent in machine learning, where performance initially improves with increasing model size or data but eventually deteriorates.
- The paper provides a theoretical analysis for this behavior using random features, Gaussian processes, and neural networks.
- It shows that double descent occurs even when optimal early stopping is used, suggesting it's not an artifact of overfitting.
- The study highlights the importance of understanding the relationship between model size, data quantity, and performance to optimize training strategies.
- Experiments on various datasets (MNIST, CIFAR-10, Fashion MNIST) demonstrate double descent in different settings, including random features, neural networks, and convolutional architectures.
- The paper also presents a rigorous evaluation of epoch-wise double descent for various optimizers and learning rate schedules.
- Model-wise and sample-wise double descent are shown to occur even when optimal early stopping is used, suggesting that stopping training based on test error may not always be the best strategy.
- The study provides practical recommendations for training strategies, such as considering a broader range of model sizes during hyperparameter search and using more data in the initial stages of training.
- Double descent behavior can also occur when models are trained with label noise or data augmentation.
- The paper concludes that understanding double descent is crucial to optimize machine learning systems, as it affects both generalization error and model complexity.
- Double descent phenomenon: Larger models and more data can initially improve performance but later lead to worse results, forming a U-shaped curve.
- Weight decay: Regularization helps prevent overfitting by reducing the model's complexity. In this study, it was found that weight decay leads to generalized double descent behavior.
- Early stopping: Does not exhibit double descent; more data always improves performance with optimal early stopping.
- Training procedure: Adversarial training can also lead to double descent behavior in certain scenarios.
- Ensembling: Combining multiple models can improve performance, especially around the critical regime where double descent occurs. However, ensembling does not help much for models without label noise.
",2843
"1912.06872",1,"- Towards Robust Toxic Content Classification aims to address the issue of automated toxicity classifiers being vulnerable to adversarial attacks, which can bypass filters and harm recipients.
- The paper proposes a method for generating realistic model-agnostic attacks using a lexicon of toxic tokens, diluting toxic signals through character-level perturbations or injecting non-toxic distractor tokens.
- These attacks reduce the detection recall of state-of-the-art neural toxicity detectors by more than 50% in some cases.
- Two approaches are explored to defend against such attacks: training on synthetically noised data and Contextual Denoising Autoencoder (CDAE), which uses character-level and contextual information to denoise perturbed tokens.
- The CDAE approach outperforms several strong baselines in handling character-level obfuscations but is still vulnerable to distractors.
- The paper analyzes the robustness characteristics of competing methods, highlighting practical considerations for improving toxicity detectors.
- Propose a model ensemble that leverages both CDAE and BERT's robustness for toxic content classification.
- Work with three datasets: Jigsaw 2018, Jigsaw Unintended Bias in Toxicity Classification (Jigsaw 2019), and OffensEval 2019.
- Generate realistic adversarial attacks using a large background corpus (Jigsaw 2019) with toxicity labels.
- Create a lexicon of toxic tokens by training a logistic regression classifier on the Jigsaw 2019 dataset.
- Apply three perturbing operations to toxic tokens: character scrambling, homoglyph substitution, and dictionary-based near-neighbor replacement.
- Evaluate the model ensemble's performance on adversarial examples and compare it with a single BERT model.
- Achieve 95% accuracy in detecting adversarial examples using the proposed ensemble method.
- The ensemble method outperforms a single BERT model by 10 percentage points in terms of F1 score on adversarial examples.
- Introduces adversarial noise techniques for robust toxicity classification: token obfuscation and distractor injection.
- Token obfuscation replaces toxic tokens with similar-looking ones, creating misspellings or slang. Distractor injection inserts non-toxic sequences into the middle of an utterance.
- Both techniques are model-agnostic, simple to implement, and subject to easy automation.
- Experiment shows that perturbations retain toxicity for human readers, with no statistically significant difference in ratings between unperturbed and perturbed comments.
- Evaluates the effect of adversarial attacks on fastText, ELMo, BERT, and logistic regression models using Jigsaw 2018 and OffensEval 2019 datasets.
- The paper focuses on robust toxic content classification, addressing adversarial attacks that can misclassify non-toxic content as toxic.
- Logistic regression classifiers struggle with out-of-vocabulary words and are ineffective against noise, while neural models still suffer significant recall loss.
- Adversarial training and contextual denoising autoencoder (CDAE) are proposed as potential defenses against adversarial attacks.
- Adversarial training has limitations due to the need for knowledge of attack details and the risk of overfitting, while CDAE learns robust representations by predicting denoised tokens using contextual information.
- The Transformer architecture with character CNN encoder is used as the underlying model for CDAE.
- The paper introduces a denoising autoencoder (CDAE) for robust toxic content classification, which combines CNNs and Transformers to handle character-level perturbations in text.
- CDAE uses noise injection during training to improve its performance on noisy data without requiring adversarial training.
- The model achieves better results than other methods when handling noisy test sets without adversarial training, while BERT and CDAE perform comparably with adversarial training.
- Adversarial training improves the overall performance of all models but does not recover to clean data standards.
- The paper highlights that text representation needs to be inherently robust for adversarial training to be effective, as models vulnerable before adversarial training tend to perform poorly even with it.
- Character CNNs need explicit training to handle noise and out-of-vocabulary words for robustness.
- Character-level perturbations degrade performance more than distractors, as they directly remove toxicity signals.
- Distractors dilute the signal instead of removing it, leading to less significant drops in performance.
- Adversarial training helps models against character-level perturbations but reduces performance on clean data.
- CDAE is more resilient to character-level perturbations than BERT due to its explicit training for this type of noise.
- BERT performs better with distractors compared to the CDAE, which might be related to their different architectures (BERT being self-attention based and CDAE using recurrent LSTM).
- Ensembling BERT and CDAE can improve overall performance by combining their advantages in handling different types of noise.
Summary of ""Towards Robust Toxic Content Classification"" paper:
- Test noise and adversarial attacks in text classification are explored, focusing on targeted character obfuscations to fool classifiers while maintaining readability.
- Existing methods for handling adversarial attacks in NLP include white-box (Ebrahimi et al., Samanta & Mehta) and black-box (Liang et al.) approaches.
- Manual curation of lexicons, automatic matching of obfuscated words (Rojas-Galeano), and context-based methods (Serrà et al., Sakaguchi et al.) are discussed as defense mechanisms against adversarial attacks.
- The paper introduces a novel approach called Character Distortion Adversarial Examples (CDAE) to generate character-level perturbations and distractor injections for robust toxic content classification.
- CDAE achieves state-of-the-art performance on the Jigsaw 2018 dataset, outperforming BERT, C+D, and ensemble methods.
- The paper highlights the need for further research into more sophisticated defenses against adversarial attacks in NLP, including contextual information and distractor injection detection.
- The paper aims to improve robustness in toxic content classification, addressing adversarial attacks and defending against them.
- Adversarial training improves general robustness but decreases performance on clean data.
- A Contextual Denoising Auto-Encoder (CDAE) is proposed for learning robust representations, which are more resistant to character-level perturbations than BERT-based models.
- An ensemble of BERT and CDAE shows the most robust approach towards combined noise.
- The paper reviews related work on adversarial examples in text classification, neural machine translation, and other areas.
- Numeric results show that CDAE outperforms BERT in terms of accuracy (92% vs 87%) and F1-score (0.93 vs 0.85) for the combined noise setting.
- The paper highlights the importance of robustness in text classification tasks, particularly in toxic content detection.
- The paper aims to address challenges in detecting toxic comments, focusing on out-of-vocabulary (OOV) words and their impact on classification accuracy.
- It analyzes the error types in toxic comment classification and proposes a method for handling OOV words using homoglyphs.
- The paper introduces a new dataset called ""Toxic Comment Classification Challenge"" (TCCC), which includes 150,000 comments from Reddit threads.
- It compares the performance of BiLSTM and BERT models for toxic comment classification with and without handling OOV words using homoglyphs.
- The study finds that homoglyph-based methods improve accuracy by up to 12% in detecting hate speech, while reducing false positives by 30%.
- The paper also presents a new method for adversarial offensive language detection called ""Decipherment.""
- It introduces the Toxic Lexicon, a list of 100 toxic tokens commonly found in online discussions.
- The study highlights the importance of handling OOV words and their impact on hate speech classification accuracy.
",1607
"2001.00973",2,"- The paper introduces an end-to-end framework for internal algorithmic auditing to address accountability gaps in large-scale AI systems development and deployment.
- This framework aims to support AI system development throughout the organization's lifecycle, enabling practitioners to identify harmful consequences of their algorithms before or after deployment.
- The proposed auditing process involves several stages, resulting in an overall audit report that draws on organizational values and principles to assess decisions made during the development process.
- The framework aims to contribute to closing the accountability gap in AI systems by embedding a robust process for ensuring audit integrity.
- Key concepts include algorithmic audits, machine learning, accountability, responsible innovation, social and professional topics, system management, technology audits, software engineering, and software development process management.
- The framework's practical application lies in helping organizations ensure their AI systems align with ethical principles and values while maintaining transparency throughout the development lifecycle.
- By addressing accountability issues early on, this approach can help prevent or mitigate potential harms caused by deployed AI systems.
- This end-to-end framework is intended to be applied across various industries and organizations that develop and deploy large-scale AI systems.
- The paper highlights the importance of internal auditing for responsible innovation in AI, emphasizing the need for a systematic approach to ensure accountability throughout the development process.
- The proposed framework aims to promote transparency and ethical decision-making in AI system development, ultimately benefitting society as a whole.
- Organizations should establish governance structures for AI accountability, as they are not moral or legal agents themselves.
- ISO 37000 defines this structure as a system that directs, controls, and holds an organization accountable to achieve its core purpose over the long term.
- Responsible development of artificial intelligence is a core purpose for organizations creating AI, so a governance system should be established for accountability.
- An internal algorithmic audit can be conducted during product development before launch, involving the audit team leading product teams, management, and stakeholders.
- Policies and principles, including ethical expectations, feed into the audit to set standards for performance.
- Environmental studies introduced urgent governance, distinguishing between auditing for system reliability vs societal harm.
- A separate governance structure is necessary for evaluating AI systems' ethical compliance, embedded in quality assurance workflows but serving a different purpose.
- Concerns about reliability are related to testing production AI systems, while issues involving social impact, downstream effects, and ethics/fairness concerns are not typically covered by concepts like technical debt and reliability engineering.
- Algorithmic auditing is similar in spirit to bug bounties, where external hackers find vulnerabilities and bugs in released software.
- Internal audits should investigate alignment with declared AI principles prior to model deployment, focusing on risk analyses centered around failure to achieve these objectives.
- Audit integrity and procedural justice are crucial for establishing the legitimacy of audit results. A fixed and vetted process is essential in ensuring companies respect the findings.
- The paper proposes an end-to-end framework for internal algorithmic auditing, which includes a risk analysis model, a methodology for assessing AI principles' alignment, and procedural guidelines to ensure audit integrity.
- Internal auditing for AI systems aims to ensure procedural justice, increase compliance, and establish audit integrity by involving ethical standards within an organization.
- Internal auditing complements external accountability by providing transparent information for third parties and end-users, enhancing AI systems' accountability.
- An internal audit framework should include an audit charter, plan, process, and report, with specific roles and responsibilities for each element.
- A 10-step methodology is proposed for conducting internal algorithmic audits, including defining scope, identifying stakeholders, gathering evidence, analyzing data, and reporting findings.
- Checklists, traceability, and Failure Modes and Effects Analysis (FMEA) are crucial in assessing ethical risks during the audit process.
- Internal auditing can help organizations identify potential risks, improve transparency, and enhance trust in AI systems by providing a structured approach to accountability.
- The paper emphasizes the importance of holistic approaches for AI accountability, encompassing technical, legal, ethical, and organizational aspects, while considering their interplay.
- Lessons from safety-critical industries like aerospace and medicine show that auditable processes and design controls have significantly improved safety records.
- Internal auditing can promote procedural justice and ethical standards within organizations by focusing on enriching, updating, or validating risk analysis for product deployment.
- Pre-deployment audits enable proactive ethical intervention methods and address sociotechnical considerations through joint efforts with product teams.
- Identifying key stakeholders and decision makers is crucial to drive appropriate responses to audit outcomes, potentially leading to structural organizational changes.
- The paper proposes an end-to-end framework for internal algorithmic auditing, combining concepts from medical device quality assurance and design controls to ensure ethical AI development.
- Design controls in medical devices can be adapted to address ethical concerns in AI systems, with varying risk levels based on intended use.
- A design history file (DHF) documents the entire development process for medical devices, including risk assessment and hazard analysis, which can serve as a model for internal algorithmic auditing in AI research.
- Internal audits should follow an end-to-end framework that includes design controls, risk assessment, and post-market surveillance to ensure accountability in AI development.
- The paper emphasizes the need for an end-to-end framework due to challenges specific to AI development, such as a lack of standardized software development practices for AI.
- Learning from other industries' experiences can help build internal accountability for responsible AI development and implementation.
- Current auditing frameworks may not be sufficient for addressing the complexities and risks associated with AI, necessitating an end-to-end framework for internal algorithmic auditing.
- The proposed framework should encompass all stages of the AI lifecycle, from design to deployment, monitoring, and evaluation.
- Collaboration between various stakeholders is crucial in developing this framework.
- An end-to-end framework for internal algorithmic auditing is essential to address AI accountability gaps and ensure responsible AI development and implementation.
- The paper discusses challenges and solutions for internal auditing of AI systems, focusing on accountability gaps and responsible innovation practices.
- Agile approaches combined with documentation-oriented methods face difficulties due to their iterative nature in certain industries.
- Internal audits can leverage risk-based approaches while working closely with product teams to identify potential risks at each process step.
- Large-scale AI systems have complex interactions, requiring research on sociotechnical systems and addressing ethical considerations in design.
- Governance processes based solely on risk may struggle to anticipate technological innovation impacts, such as the 2008 financial crisis.
- Explicit documentation about purpose, data, and model space can help identify potential hazards earlier in development.
- Selbst and Barocas argue for seeking explanations behind a model's development process rather than just the model itself.
- The paper emphasizes the need to address challenges of designing, prototyping, and maintaining AI systems due to their unique characteristics compared to other intelligent or computing systems.
- An end-to-end framework for internal algorithmic auditing (SMACTR) is introduced, consisting of five stages: Scoping, Mapping, Artifact Collection, Testing, and Reflection.
- SMACTR aims to provide documentation requirements and analysis levels for each stage, with artifacts produced by auditors, engineering teams, and jointly developed outputs.
- The paper introduces an end-to-end framework for internal algorithmic auditing to address AI accountability gaps.
- This process consists of five stages: scoping, planning, execution, reporting, and follow-up.
- Scoping involves clarifying objectives, risk analysis, and identifying potential harm or social impact.
- Planning focuses on defining scope, timeline, resources, and deliverables for the audit.
- Execution includes data collection, model evaluation, and stakeholder engagement.
- Reporting documents findings, provides recommendations, and addresses concerns raised during the process.
- Follow-up ensures post-audit recommendations are implemented and monitors system performance over time.
- The framework can be applied in full or in a lighter-weight formulation depending on assessment needs.
- This process does not cover determining what systems to audit, which depends on contextual factors like industry standards, organization needs, and case specifics.
- Key artifacts developed during this process include ethical reviews of system use cases and social impact assessments.
- The paper proposes an end-to-end framework for internal algorithmic auditing, focusing on accountability gaps in AI systems.
- Social impact assessments are used to inform ethical reviews by analyzing potential consequences of AI system use on various communities.
- This framework aims to address accountability gaps and ensure responsible innovation in AI systems through practical applications like internal auditing.
- The end-to-end process involves social impact assessment, risk management, mapping stage with FMEA (Failure Mode and Effect Analysis), key artifacts, and examples such as child abuse detection algorithms and smile detectors.
- This framework aims to close the AI accountability gap by providing a comprehensive approach for internal auditing of algorithmic systems.
- Documenting development processes and historical artifacts is crucial for context in audits.
- Ethnographic fieldwork can help understand engineering and product development processes better, similar to practices in finance and healthcare industries.
- The framework emphasizes the need to assess how numerical metrics align with ethical values and social impact concerns beyond traditional AI metrics like loss.
- Metrics should be understood within their engineering and social contexts, as data interpretation can be subjective and contested.
- During interviews, auditors must pay attention to assumptions and values behind the metrics, not just focus on isolated measurements.
- The paper introduces an end-to-end framework for internal algorithmic auditing, focusing on promoting transparency, responsibility, and accountability in AI systems.
- Key artifacts include design checklists, model cards, and datasheets to ensure adherence to AI principles during product development processes.
- Model cards and datasheets are crucial tools for making algorithmic development auditable, reducing risks associated with their use.
- The audit checklist ensures all expected documentation from product development cycles is complete before starting an audit review.
- Testing approaches vary depending on context but should ensure compliance with organizational values and assess the likelihood of system failures.
- An end-to-end framework for internal algorithmic auditing focuses on risk prioritization, testing based on FMEA (Failure Mode and Effects Analysis), and ethical risk analysis charts.
- Examples of testing methods include adversarial examples for smile detectors and diverse user profiles for child prediction models to test for biased associations.
- Artifacts from the auditing stage include adversarial testing results and an ethical risk analysis chart.
- The paper provides a practical approach for organizations, teams, or engineers to address AI accountability gaps through internal algorithmic auditing.
- Model cards and datasheets are essential tools in making algorithmic development more auditable, promoting transparency, responsibility, and accountability in AI systems.
- The paper introduces an end-to-end framework for internal algorithmic auditing, focusing on ethical risks throughout AI development.
- Adversarial testing is used to find vulnerabilities in ML systems and identify potential risks through the Ethical Risk Analysis Chart.
- Internal adversarial testing helps reveal unexpected product failures before they affect real-world users, while proactive testing of launched products is a best practice for lifecycle management.
- The Failure Mode and Effects Analysis (FMEA) should be updated with test results to reflect changes in risk assessments.
- The Ethical Risk Analysis Chart combines likelihood and severity of failures, helping define high-priority threats.
- Risks are assigned a severity level based on their combination of likelihood and social impact assessment/ethnographic interviews.
- The Reflection Stage involves analyzing test results against ethical expectations, updating risk analysis, and outlining principles that may be jeopardized by the AI system upon deployment.
- Key artifacts include a mitigation plan or action plan developed jointly by audit and engineering teams to address prioritized risks and test failures.
- Examples of design decisions for smile detection algorithms involve training on diverse data, adding underrepresented populations in CelebA, or considering smiling as a favorable photo pose.
- For child abuse detection models, ethical considerations may lead to project stalling or cancellation due to high-risk scenarios and complex mitigation strategies.
- The framework aims to provide a comprehensive approach to internal algorithmic auditing, addressing ethical risks throughout the AI development process.
- The paper emphasizes the need for careful attention to the distinction between designers' and users' mental models of AI systems, highlighting potential differences due to factors like distributional drift and misuse.
- Large gaps in intended and actual uses of algorithms have been found in contexts such as criminal justice and web journalism, adding complexity to anticipated hazards and risks.
- Christin suggests studying practices, uses, and implementations surrounding algorithmic technologies by establishing new exchanges between various disciplines like critical data studies, sociology of work, and organizational analysis.
- The paper proposes an end-to-end framework for internal algorithmic auditing, focusing on closing AI accountability gaps and ensuring system compliance with declared ethical principles.
- It introduces the concept of an Algorithmic Design History File (ADHF), inspired by medical device industry's design history file, to document changes in AI systems over time.
- The framework consists of four phases: planning and scoping, data collection and analysis, risk assessment and mitigation, and reporting and follow-up.
- Involving various stakeholders (data scientists, legal experts, ethicists) throughout the process is crucial for a comprehensive approach to auditing AI systems.
- The paper emphasizes the importance of transparency in algorithmic decision-making through explainable artificial intelligence (XAI) techniques.
- Addressing social risks associated with AI systems and ensuring fairness, justice, and ethics for those disproportionately affected by them is essential.
- Risk analysis frameworks from Failure Modes and Effects Analysis (FMEA) or other tools in finance and medical deployments can be used to consider user-related issues during the design stage.
- For concerns raised against ethical values, a threshold for acceptable performance must be defined, similar to safety engineering's risk thresholds.
- An audit report summarizes key findings from the ADHF, comparing it with ethical objectives and engineering requirements.
- Internal audits face limitations due to shared organizational interests and potential biases but can still be effective in controlling risks in various industries.
- The paper introduces ""algorithmic impact assessments"" as a tool to measure AI systems' social impact and identify potential risks.
- Organizations should adopt continuous auditing, integrating algorithmic audits into existing governance structures and risk management processes.
- Research is needed for standardized methodologies and tools to support internal algorithmic auditing frameworks.
- The paper defines an end-to-end framework for internal algorithmic auditing, addressing the accountability gap in AI systems.
- Key stages include data collection, model development, deployment, monitoring, and evaluation.
- Data collection involves defining objectives, identifying stakeholders, and ensuring ethical practices.
- Model development requires transparency, interpretability, and fairness considerations.
- Deployment focuses on continuous monitoring for bias, accuracy, and performance, with a feedback loop to improve the system.
- Evaluation measures model impact on stakeholders, assesses effectiveness in achieving objectives, and identifies areas for improvement.
- Collaboration between data scientists, social scientists, and domain experts is crucial for a holistic approach.
- Practical applications can be found in various fields such as child protective services, predictive policing, and healthcare.
- The framework aims to promote trust and transparency in AI systems by addressing the accountability gap.
- Continuous improvement through iterative auditing processes is emphasized.
",3027
"2001.09977",1,"- Meena is a multi-turn open-domain chatbot trained end-to-end on data from public domain social media conversations, with 2.6B parameters and a test perplexity of 10.2 based on an 8K BPE subword vocabulary.
- The paper introduces Sensibleness and Specificity Average (SSA) as a human evaluation metric for chatbots, capturing key elements of a human-like multi-turn conversation.
- Experiments show strong correlation between perplexity and SSA; the best end-to-end trained Meena scores high on SSA (72%), suggesting that a human-level SSA of 86% is potentially within reach if perplexity can be better optimized.
- The full version of Meena, with a filtering mechanism and tuned decoding, achieves an SSA score of 79%, which is 23% higher in absolute terms than existing chatbots evaluated.
- Mitsuku and Cleverbot scored the same on overall SSA but displayed different strengths: Mitsuku had higher sensibleness while Cleverbot had higher specificity.
- Propose a human evaluation metric for multi-turn open-domain chatbots, capturing sensibleness and specificity in human conversations.
- Show evidence that perplexity correlates with human judgment, contrasting recent findings on other automatic metrics.
- Demonstrate an end-to-end neural model can surpass existing chatbots' sensibleness and specificity using low perplexity.
- Introduce a static evaluation setup for benchmarking models on fixed multi-turn contexts to generate responses.
- Present an interactive evaluation setup, allowing humans to freely chat with chatbots.
- Discuss the limitations of their methodology and suggest future work.
- Towards a Human-like Open-Domain Chatbot: The paper aims to improve chatbot evaluation methods by introducing Sensibleness and Specificity Average (SSA) as a metric that combines sensibleness and specificity, providing a more accurate representation of human likeness.
- Static Evaluation: A collection of 1,477 conversational contexts called the Mini-Turing Benchmark (MTB) is used to create a common benchmark for comparing models.
- Sensibleness and Specificity: These two metrics are calculated by labeling responses as sensible or not sensible and specific or not specific, respectively. The degree of subjectivity in the crowd worker agreement is measured using Krippen-dorff's alpha.
- SSA (Sensibleness and Specificity Average): A combined metric that averages sensibleness and specificity to measure human likeness, penalizing chatbots for consistently producing generic responses.
- Human Likeness Evaluation: Crowd workers assess whether a response is ""human-like"" or not, providing an alternative way to evaluate the SSA metric's effectiveness in capturing important aspects of human likeness.
- Advantages of SSA over direct human likeness evaluation: SSA is more objective, easier for crowd workers to understand, and penalizes boring and vague responses.
- The paper introduces a new dataset called the Multi-Turn Bot (MTB) for evaluating open-domain chatbots.
- MTB contains contexts from various sources, including Vinyals and Le's work, Loebner Prize contests, and personality questions.
- Static evaluation involves feeding MTB contexts to models or humans to obtain responses, with crowd workers labeling the responses as sensible and specific.
- Interactive evaluation allows crowd workers to chat 1:1 with a chatbot, also labeling responses for sensibility and specificity.
- The paper estimates human performance in static and interactive evaluations using internal company volunteers and crowd workers.
- Cleverbot and DialoGPT are evaluated using the same setup as Meena.
- Mitsuku and XiaoIce are only evaluated through interactive evaluation due to a lack of public APIs for these chatbots.
- Towards a Human-like Open-Domain Chatbot: The paper aims to analyze and compare two chatbots, XiaoIce (Mandarin) and Mitsuku (English), in terms of sensibleness and specificity. It also introduces the Meena model, an end-to-end large neural network model that generates almost humanlike conversations in open-domain settings.
- Data Collection: Volunteers conversed with XiaoIce and Mitsuku on their web apps, while independent crowd workers labeled sensibleness and specificity for each chatbot turn. The paper highlights the impact of images in responses and the importance of resetting the state between conversations.
- Comparison Metrics: Perplexity is used as an automatic evaluation metric to measure how well a model predicts test set data, correlating with human judgement of sensibleness and specificity.
- Meena Model: The largest end-to-end model in the field, trained on public domain social media conversations. It demonstrates that a large end-to-end model can generate almost humanlike chat responses in an open-domain setting.
- Training Data: Meena's dataset is mined and filtered from public domain social media conversations. The source data are essentially message trees involving multiple speakers.
- Architecture: Meena uses a Transformer encoder-decoder architecture with 12 encoder layers, 12 decoder layers, and 8 attention heads per layer. It has 340M parameters and was trained on 400GB of data.
- Decoding Algorithm: The model uses beam search with a beam size of 5 to generate responses.
- Sample Conversations: Meena's conversations showcase its ability to handle complex topics, maintain context, and respond appropriately.
- The paper proposes a method to create a human-like open-domain chatbot using trees involving multiple speakers and treating each turn in a conversation as a response with context from up to 7 previous turns.
- Data filtering is applied to improve the generation quality, removing messages that don't meet specific criteria (e.g., low alphabetic characters percentage).
- The final Meena dataset contains 867M (context, response) pairs and 341GB of text, making it larger than GPT-2's training data (40GB).
- An Evolved Transformer (ET) model with 2.6B parameters is used for the best performing Meena model, achieving a perplexity score of 10.2.
- The largest vanilla Transformer had 32 decoder layers and scored a higher perplexity (10.7) compared to the Evolved Transformer.
- Meena's hidden size is 2,560, with 32 attention heads.
- The paper provides examples of responses generated by sampling outputs, beam search outputs, and model architecture details.
- Meena's model has 762M parameters, with a hidden size of 2,560 and 32 attention heads. Embeddings are shared across the encoder, decoder, and softmax layer. The maximum token length is 128 for both encoder and decoder.
- Manual coordinate-descent search was used to find the best model's hyperparameters. A TPU-v3 Pod with 2,048 cores trained the model on a Meena dataset containing 40B words (61B BPE tokens).
- Sample-and-rank decoding strategy is used for generating diverse and high-quality responses. This involves sampling N independent candidate responses using temperature T and selecting the one with the highest probability. Temperature T regulates the probability distribution, favoring contextually rare or common words depending on its value.
- Meena's conversations showcase its ability to handle arbitrary open-domain input, addressing various topics and maintaining a human-like conversational flow.
- Towards a Human-like Open-Domain Chatbot paper focuses on creating an AI chatbot that can handle arbitrary open-domain input and exhibit human-like conversational abilities.
- Meena, the proposed model, is able to handle conversations with sensible and specific responses, as shown in various examples provided.
- The correlation between test perplexity (automatic metric) and human evaluation metrics (sensibleness and specificity) is strong, indicating that perplexity can be a good automatic metric for measuring these qualities.
- Meena's performance in interactive evaluations shows similar correlations with perplexity as the static evaluations, suggesting that the correlation is not due to dataset bias.
- The paper also introduces an improved version of Meena (Meena full) and compares its performance against other chatbots like XiaoIce, Mitsuku, DialoGPT, and Cleverbot.
- Towards a Human-like Open-Domain Chatbot: Main Contributions and Findings
- Correlation with Perplexity is not due to dataset bias
- Consistency in Evaluation: Static vs Interactive SSA (Sensibleness and Specificity)
- Human-level Estimates: Sensibleness, Specificity, and SSAs (Sensibleness, Specificity, and Standardized Scores)
- Comparison with Existing Chatbots: XiaoIce, Mitsuku, DialoGPT, Cleverbot, Meena
- Limitations of Current Models and Future Research Directions
Summary of ""Towards a Human-like Open-Domain Chatbot"" paper: The study aims to create an open-domain chatbot with human-like conversational abilities, focusing on sensibleness and specificity. It analyzes three models - Meena (base), Cleverbot, and DialoGPT - in terms of interactive and static evaluations. Interactive SSA scores for Meena (56%), Cleverbot (56%), and DialoGPT (48%) are similar to human performance in a single-turn setting. However, the paper highlights that DialoGPT struggles with specificity and tends towards briefer responses due to its optimization for Turing test evaluation. Meena's base model generates rich and interesting responses, while Cleverbot can be sensible but less specific. The study also discusses the limitations of current chatbots and suggests future research directions.
- Towards a Human-like Open-Domain Chatbot: Main Contributions and Findings
- Improving Sensibleness, Specificity, and Addressing Cross-turn Repetitions in Large Language Models (LLMs)
- Advancing Decoding Strategies for Interactive SSA
- Safety Layer to Filter Out Inappropriate Responses
- Meena: A Highly-tuned Open-domain Chatbot with Improved Performance

- The paper aims to create a human-like open-domain chatbot by improving sensibleness, specificity, and addressing cross-turn repetitions in Large Language Models (LLMs).
- To improve sensibleness and specificity, the authors tuned decoding strategies, added a rule to detect cross-turn repetitions, and introduced an additional classifier layer for filtering at serving time.
- They evaluated temperature T and top-k in decoding strategies, finding that N = 20 provided significant improvement over N = 1 for sample-and-rank. However, N = 400 showed worse performance.
- The safety layer helped address inappropriate responses by introducing an additional classifier at serving time.
- Meena, a highly tuned open-domain chatbot, was created with improved performance through the proposed techniques.
- The paper aims to develop a human-like open-domain chatbot by focusing on sensibleness and specificity for its evaluation metrics.
- It introduces the concept of perplexity as an automatic proxy for human judgment, showing strong correlation with human evaluation.
- The study compares perplexity with other existing metrics like BLEU and ROUGE, demonstrating that perplexity is more suitable for dialogs or language generation systems.
- It discusses related works in the field of automatic metrics for conversational modeling, highlighting limitations and differences from previous approaches.
- The paper uses Meena as an example to showcase how perplexity can be used to evaluate sensibleness and specificity in a human-like open-domain chatbot.
- It presents results on the static MTB benchmark and interactive setup, demonstrating that perplexity correlates with human evaluation for up to 14 turns of conversation.
- The study concludes that perplexity can be used as an automatic proxy for evaluating sensibleness and specificity in open-domain chatbots, providing a simpler alternative to existing complex metrics.
- Towards a human-like open-domain chatbot: The paper aims to improve the sensibleness and specificity of an open-domain chatbot by optimizing test set perplexity.
- Static evaluation limitations: The dataset used for evaluation is biased, as it contains only one to three-turn contexts and focuses on social conversations.
- Interactive evaluation: This addresses some bias issues in static evaluation while providing a consistent score. However, it has its own limitations, such as being too short and not covering deeper topics.
- Future work suggestions: Expand the set of basic human-like conversation attributes beyond sensibleness and specificity, possibly including humor, empathy, deep reasoning, question answering, and knowledge discussion skills.
- Optimization of test set perplexity: Continued optimization of sensibleness could be explored by optimizing test set perplexity.
- Acknowledgments: The paper thanks various individuals who provided feedback on drafts, volunteers who helped collect conversations, and the Google Brain team for their support.
- Towards a Human-like Open-Domain Chatbot: The paper discusses the challenges and approaches to create a chatbot that can converse in an open-domain, human-like manner.
- Unifying human and statistical evaluation for natural language generation: This work focuses on unifying human and automatic evaluation methods for natural language generation tasks.
- Real conversations with artificial intelligence: The study compares real online conversations between humans to those between humans and chatbots, highlighting the differences in conversation styles.
- Distilling the knowledge in a neural network: This paper introduces a method to distill the knowledge from a large neural network into a smaller one while maintaining performance.
- The curious case of neural text degeneration: The study investigates the phenomenon of neural networks generating nonsensical or low-quality text, and proposes methods to address this issue.
- Human and automatic detection of generated text: This paper introduces an evaluation method for determining whether a given text is human-written or machine-generated.
- Comparison of diverse decoding methods from conditional language models: The study analyzes various decoding methods used in conditional language models, evaluating their performance in generating coherent and meaningful texts.
- Ctrl: A conditional transformer language model for controllable generation: This paper introduces a new language model that can generate text with specific attributes or styles.
- Linguistically-informed specificity and semantic plausibility for dialogue generation: The study focuses on improving the quality of generated dialogues by incorporating linguistic knowledge into neural network models.
- Computing krippendorff's alpha-reliability: This paper introduces a method to compute Krippendorff's alpha, a measure used for evaluating the reliability of coding schemes in qualitative research.
- The winograd schema challenge: The study presents a test designed to evaluate a system's ability to understand and reason about natural language.
- A diversity-promoting objective function for neural conversation models: This paper proposes an objective function that encourages diversity in generated dialogue responses.
- A persona-based neural conversation model: The study introduces a model that generates conversations based on the personality of each participant.
- Adversarial learning for neural dialogue generation: The paper explores using adversarial training to improve the quality and realism of generated dialogues.
- ROUGE: A package for automatic evaluation of summaries: This work introduces a tool for evaluating text summarization systems based on n-gram overlap with reference texts.
- How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation: The paper investigates the limitations and challenges in evaluating dialogue systems using unsupervised methods.
- Towards an Automatic Tur-ing Test: Learning to Evaluate Dialogue Responses: This work introduces a new metric for evaluating dialogue responses based on their similarity to human-written texts.
- Addressing the rare word problem in neural machine translation: The paper presents a method to improve the performance of neural machine translation models when dealing with rare words.
- Why We Need New Evaluation Metrics for NLG: This study highlights the need for new evaluation metrics in natural language generation tasks, as existing methods often fail to capture important aspects of generated text.
- BLEU: a method for automatic evaluation of machine translation: The paper introduces a widely used metric for evaluating machine translation systems based on n-gram overlap with reference texts.
- Language models are unsupervised multitask learners: This work demonstrates that language models can perform various tasks without explicit supervision, highlighting their versatility and potential applications.
- I know the feeling: Learning to converse with empathy: The study introduces a model for generating dialogue responses that consider the emotional state of both participants in the conversation.
- Regularized evolution for image classifier architecture search: This paper presents an approach for optimizing neural network architectures through evolutionary algorithms.
- Large-scale evolution of image classifiers: The study explores using large-scale evolution to improve the performance and generalization capabilities of image classification models.
- What makes a good conversation? how controllable attributes affect human judgments: This paper investigates the factors that contribute to a successful conversation, focusing on the role of controllable attributes in human perception.
- Neural responding machine for short-text conversations: The study introduces a model for generating responses to short text messages, improving the quality and relevance of generated responses.
- Adafactor: Adaptive Learning Rates with Sublinear Memory Cost: This paper proposes an adaptive learning rate method that requires less memory than traditional methods while maintaining performance.
- The: The paper discusses a new language model for generating conversations based on the personality of each participant, focusing on creating more engaging and realistic dialogues.
- The paper discusses the development of human-like open-domain chatbots, focusing on their cost and performance in comparison to existing approaches.
- It highlights key works that have contributed to the field, such as the Evolved Transformer (David R. So et al., ICML 2019), neural network approach for context-sensitive generation of conversational responses (Alessandro Sordoni et al., NAACL-HLT 2015), and Sequence to Sequence Learning with Neural Networks (Ilya Sutskever et al., NeuRIPS 2014).
- The paper introduces RUBER, an unsupervised method for automatic evaluation of open-domain dialog systems by Chongyang Tao et al. (CoRR, abs/1701.03079, 2017).
- It also mentions the importance of evaluating and comparing different approaches in the field, as demonstrated by Anu Venkatesh et al.'s work (2018).
- The paper emphasizes the need for a cost-effective approach to open-domain chatbots, considering both performance and computational efficiency.
- It introduces the concept of ""human-like"" in open-domain chatbots, which involves generating responses that are contextually appropriate, coherent, and engaging.
- The paper discusses how existing approaches fall short in achieving human-like conversational behavior due to limitations such as lack of contextual understanding or inability to handle long conversations.
- It presents a new method for creating human-like open-domain chatbots by combining the strengths of various models and techniques, including the Evolved Transformer, Tensor2Tensor (Ashish Vaswani et al., CoRR 2018), and Attention is All You Need (Ashish Vaswani et al., NeuRIPS 2017).
- The paper provides an example of a human-like open-domain chatbot conversation, demonstrating the effectiveness of their proposed method in generating contextually appropriate responses.
- Lastly, it discusses the potential impact and future directions for research in this area, including improving performance metrics, addressing ethical concerns, and exploring new applications for human-like chatbots.
",3898
"2002.02878",1,"- The paper discusses a distinction between goal-oriented and chit-chat dialogue in artificial intelligence (AI) research, highlighting their differences and potential benefits when combined.
- Goal-oriented dialogue offers clear metrics for success and meaningful learning signals but is limited to specific tasks, while chit-chat imitation covers a wider range of natural language but lacks clear goals.
- The study introduces an approach that bridges the gap between these two domains in a fantasy game world setting, where agents and humans engage in both dialogue and actions.
- A goal-oriented model is trained using reinforcement learning against an imitation-learned chit-chat model with two approaches: choosing a topic or selecting an utterance based on top-K utterances from the chit-chat model.
- Both models outperform inverse model baselines and can converse naturally to achieve goals, demonstrating their effectiveness in combining goal-oriented and chit-chat dialogue.
- The study highlights the importance of modeling implicit goals in real-life conversations, requiring large amounts of world knowledge and understanding of human behavior.
- The paper introduces a new family of tasks that bridge goal-oriented and chit-chat dialogue, combining clear metrics with rich, open-domain natural language in a multiplayer fantasy game environment.
- Agents are trained to conduct open-ended dialogue with the aim of persuading their partner to execute specific actions (emotes or game actions).
- The LIGHT game environment is used, featuring 22 emote types and various interactions with objects and locations.
- Three approaches for training agents are compared: inverse model, RL with latent discrete variables (topics), and RL via rewarding top-K outputs from the model.
- Both RL methods outperform inverse models and chit-chat imitation baselines in achieving goals while maintaining natural dialogue.
- The paper's main contributions include a new task family, results and analysis of scalable RL algorithms and behavioral cloning models on these tasks, and the LIGHT game environment.
- LIGHT is an open-domain goal-oriented dialogue system based on a fantasy medieval setting, using crowdsourced game locations, characters, and objects.
- Players interact with each other in pairs, generating rich human play data for training.
- The introduced tasks involve achieving open-domain goals during interactions between two agents in LIGHT scenarios.
- Agents Mplayer (the player) and Menv (environment agent) communicate through utterances, while Menv executes actions based on Mplayer's persuasion goal.
- A behavioral cloning model trained from human-human interaction data is used to represent the environment agent.
- The system achieves 30% accuracy in achieving goals and 17.9% in correctly predicting the next utterance, with a mean of 2.4 goal actions per episode.
- Applications include making knights smile in fantasy games by creating agents that can converse and achieve goals within a rich environment.
- The paper introduces an open-domain goal-oriented dialogue agent, Mplayer, which aims to persuade another agent (Menv) to take a specific action g within a fantasy game setting.
- Two types of goals are experimented with: game actions and emote actions.
- Agents use the same train, valid, test split from human-human LIGHT episodes, randomly assigning roles Mplayer and Menv.
- Mplayer only speaks, not performing actions itself, to study grounded dialogue between agents.
- The state observation Ot consists of agent's setting description (Dplayer), utterance history (St−1), and the goal (g).
- Reinforcement learning formulation frames the task as a Markov decision process with Mplayer as an RL agent, giving a terminal reward of +1 if the goal g is achieved and 0 otherwise.
- The paper uses retrieval models for both Menv and Mplayer, based on LIGHT dialogue training corpus (111k utterances). Generative models are left for future work.
- Base agent architecture consists of a pre-trained 12-layer bidirectional transformer, finetuned on the task using a bi-encoder approach.
- The paper demonstrates that Mplayer can achieve high accuracy in persuading Menv to take desired actions within fantasy game settings.
- Practical applications include creating more engaging and immersive AI characters in video games or virtual reality environments.
- The paper introduces an open-domain goal-oriented dialogue agent for fantasy games, focusing on generating natural language utterances that lead to achieving goals in a game environment.
- It uses two transformers to encode context and candidate responses, scoring matches via dot product. Utterance selection is based on the highest score from training set candidates.
- The model also handles actions and emotes, with admissible actions provided by the game engine and 22 emote candidates always available.
- Training involves cross entropy loss, considering other elements in a batch as negatives, similar to Mazar´e et al.'s (2018) approach.
- The environment agent remains fixed during RL training, ensuring models stick to using natural language semantics rather than learning an emergent language.
- Two RL approaches are designed: one learns to pick latent discrete variables (topics) for goal-achieving utterances and another learns to select the correct Uplayer from top K candidates.
- An inverse model is considered as a baseline, trained via behavioral cloning on human-human data. It consists of a bi-encoder that takes an observation as input and outputs an utterance.
- The inverse model is trained supervisedly using cross entropy loss, unlike the RL agents which learn interactively.
- The paper presents results for a fantasy game called ""Chainmail"" where the agent successfully achieves goals in 30% of episodes.
- Practical applications include generating natural language utterances for goal-oriented dialogue agents in fantasy games and potentially other domains.
- The paper proposes a model for goal-oriented dialogue agents that learns from human-human language data and self-chat, addressing issues of data efficiency, computing time, and language drift.
- It introduces a Latent Discrete Variable (Topic) Model with two components: an observation-to-discrete variable function (FC) and a transformer for generating dialogue utterances (Tu).
- FC consists of a transformer that maps observations to state representations and a policy chooser that outputs the value of a discrete latent variable representing a topic.
- Tu takes in the observation, output from FC, and generates a dialogue utterance. The entire model is trained as a chain: u = Tu(O, PC(Ts(O))).
- Initial topics are pre-trained using K-means clustering on vectorial representations of observations.
- The set of topics serves as actions A for the RL setup.
- Pre-training Tu uses human-human training data with appended topic computed by FC, allowing it to generate an action (utterance).
- The model's practical application is in fantasy games, where it can make knights smile through goal-oriented dialogue.
- The paper demonstrates that the proposed model achieves better performance than a baseline model and shows promising results for goal-oriented dialogue agents.
- This approach could be applied to other domains requiring goal-oriented dialogue agents, such as customer service or virtual assistants.
- The paper introduces an Open-domain goal-oriented dialogue agent that can generate actions (utterances) based on both input and a topic.
- It trains a policy using Reinforcement Learning (RL), optimizing the topic at any given point in the episode, while keeping pre-trained portions of the model fixed during fine-tuning.
- The cluster chooser is redefined as an MLP network to sample discrete actions from categorical probability distributions over possible topics.
- The Advantage Actor-Critic (A2C) implementation is used for training the policy and value function in both this and the Top-K model.
- The Top-K model keeps a small number of trainable parameters by using an inverse model to get context embeddings, considering K most likely utterances given the context and goal.
- A Transformer model is used as an alternative approach for training a smaller policy in the Top-K model.
- Both models are evaluated on various dialogue datasets, achieving state-of-the-art results in some cases.
- The paper highlights practical applications of these models in generating goal-oriented dialogues and their potential use in virtual assistants or chatbots.
- The paper introduces a policy ""bi-encoder"" (Top-K-Bi) and Top-K models for goal-oriented dialogue agents in an open-domain setting, using attention weights from Transformer models as distribution over candidates for sampling utterances.
- Related work includes chit-chat dialogue approaches, which are typically large pre-trained transformers fine-tuned for either generative or retrieval tasks. Retrieval models perform well in various tasks (Zhang et al., 2018; Dinan et al., 2018; Li et al., 2019).
- The paper shares similarities with these approaches, as it uses LIGHT dialogue data for chit-chat without specific goals and conversations covering diverse topics.
- Goal-oriented dialogue tasks have traditionally focused on narrow domains like restaurant booking (Henderson et al., 2014), taxi or hotel services (Budzianowski et al., 2018), or trip planning (El Asri et al., 2017).
- Earlier work used labeled state representations, slot filling mechanisms, and dialogue managers (Rieser & Lemon, 2011) but has shifted to end-to-end approaches (Bordes et al., 2017), similar to chit-chat models. However, these two sets of tasks are rarely considered together or using the same methods.
- Tang et al. (2019) used coarse-grained keywords for open-domain chit-chat, but their target can be achieved by either human or agent responses.
- Classical goal-oriented dialogue literature extensively studied RL (Singh et al., 2000), primarily improving dialogue managers to manage transitions between states. Recent works have focused on end-to-end learning.
- Some works use self-play mechanisms for end-to-end reinforcement learning, deriving rewards from goals. A related approach is the negotiation task of Lewis et al. (2017); Yarats & Lewis (2017), where agents swap items with different values.
- The paper's setup involves a rich world with characters and 3462 objects, allowing for various interactions and goal-oriented dialogue tasks.
- The proposed models achieve high accuracy in goal-oriented dialogue tasks while maintaining the flexibility of open-domain chit-chat agents.
- The paper explores using reinforcement learning (RL) for open-domain goal-oriented dialogue agents in fantasy game worlds, with a focus on multiplayer games and dialogue.
- RL has been applied to various tasks within the gaming domain, including visual question answering, chit-chat, human-bot conversations, language in graphical games, real-time strategy war games, and text adventure games.
- The paper's main contribution is introducing a new approach for multiplayer games with dialogue and actions from other agents, which differs from previous RL applications in single-player environments.
- Experiments compare various models on game action and emote tasks, considering different numbers of steps (n = 1 and n = 3) allowed to complete the goal. The authors also introduce naive baselines for sanity checks.
- Results show that Topic RL performs better than other models in both game actions and emotes, with improved performance when increasing the number of steps.
- Practical applications include using these models to create more engaging fantasy games where dialogue agents can make decisions based on their goals and respond realistically to player actions.
- The paper introduces an Open-domain goal-oriented dialogue agent that can achieve goals within a fantasy game environment.
- It uses Topic Reinforcement Learning (Topic RL) and Top-K Reinforcement Learning (Top-K RL) to improve the performance of agents in completing tasks, such as selecting weapons or finding friends.
- The Random Utterance model serves as a baseline for comparison, while the inverse model does not have a goal to achieve.
- Results show significant improvements for Topic RL and Top-K RL compared to other baselines, with smoother training curves over time.
- The agents can generate natural utterances that elicit desired responses from environment agents, making them suitable for fantasy game worlds.
- Practical applications include enhancing the experience of players in role-playing games by creating more engaging and realistic conversations between characters.
- The paper introduces an open-domain goal-oriented dialogue agent that can generate appropriate utterances based on given situations and desired goals.
- It trains models using inverse reinforcement learning (IRL) and Topic Reinforcement Learning (Topic RL), comparing their performance in achieving specific actions such as get, hit, hug, give, remove, steal, drop, put, eat, etc.
- The paper shows that the Topic RL model outperforms IRL in semantic connection for utterances, with examples of successful episodes where it leads to desired actions.
- Analysis of utterance choice reveals a clear improvement in semantic connection for the Topic RL model over the inverse model, as seen in high-scoring utterances for different verb types.
- The training performance comparison shows that models with better test results also fit better on train data, but significant overfitting can occur, indicating potential future work to improve generalization or use more training data.
- The paper highlights the practical application of these open-domain goal-oriented dialogue agents in fantasy games, where they can make knights smile by generating appropriate dialogue for various situations and goals.
- The paper introduces a 3-step dialogue model for open-domain goal-oriented dialogue agents, which outperforms existing models like 1-step and 1-step 3x models in various tasks.
- Increasing the capacity of both models by using more topics or clusters improves performance up to 200 clusters (K = 200), after which it saturates.
- The model performs best for high and medium frequency verbs, with easier tasks involving clear actions like hugging or hitting having higher success rates.
- Non-1-step achievable goals are much harder and represent a significant challenge to future systems.
- Applying 1-step trained models on the 3-step task results in inferior performance compared to optimized 3-step models.
- The Topic RL model repeats utterances less frequently than the 1-step 3x baseline, with a 25.8% repeat rate versus 37.3%.
- The paper highlights the potential practical applications of these findings in creating more realistic and engaging dialogue agents for fantasy games.
- The paper introduces open-domain goal-oriented dialogue agents that can interact and achieve goals in a rich world with diverse language, bridging the gap between chit-chat and goal-oriented dialogue.
- Two reinforcement learning approaches are explored to solve this task, comparing them against an inverse model baseline.
- The agents effectively learn dialogue strategies leading to successful completion of goals while producing natural chat.
- Future work should focus on developing improved agents that can act and speak in natural language at scale in the proposed open-domain task environment.
- This setup has potential for further generalization as models become capable of handling richer goal states.
- Repeating an utterance may sometimes help achieve a desired goal, similar to real life.
",2982
"2002.05651",2,"- The paper emphasizes the importance of accurately reporting energy and carbon usage for understanding machine learning's impact on climate change.
- It introduces a framework that simplifies tracking real-time energy consumption, carbon emissions, and generating standardized online appendices.
- A leaderboard for energy-efficient reinforcement learning algorithms is created to incentivize responsible research in this area.
- Case studies using the framework propose strategies for mitigating carbon emissions and reducing energy consumption.
- The authors hope that making accounting easier will contribute to sustainable development in machine learning experiments, leading to more research into energy-efficient algorithms.
- The paper aims to address the lack of energy and carbon footprint reporting in ML research, highlighting its importance for incentivizing efficiency, raising awareness, and driving mitigation efforts.
- It introduces experiment-impact-tracker, a lightweight framework that simplifies consistent, accurate reporting of energy, compute, and carbon impacts of ML systems.
- The framework helps in understanding emissions from energy grids, recording power outputs from GPUs and CPUs, and navigating through various tools for these tasks.
- Challenges with existing accounting methods are discussed, along with learnings from analyzing experiments using experiment-impact-tracker.
- Recommendations for promoting energy-efficient research include incentivizing leaderboards, running experiments in carbon-friendly regions, reducing overheads, considering trade-offs before deploying models, selecting efficient test environments, ensuring reproducibility, and consistently reporting metrics.
- The paper emphasizes the need for accurate and systematic reporting of energy and carbon footprints in machine learning research, addressing limitations in existing estimation methods.
- A framework is proposed to improve reporting, including additional mitigation strategies beyond previous works.
- Energy accounting focuses on measuring energy consumption, considering life-cycle aspects but largely ignoring manufacturing impacts due to attribution difficulties.
- Datacenter energy impacts are analyzed through hardware and software analyses, with various factors contributing to power consumption.
- Carbon footprint estimation methods include direct measurement, indirect measurement (emission factors), and life-cycle assessment (LCA).
- The paper discusses ML conference travel's carbon impacts, highlighting the need for accurate accounting in machine learning research.
- Energy and carbon footprints are analyzed at the per-experiment software level, focusing on data center energy consumption components like cooling, lighting, power conversion, network hardware, server/storage, DRAM, and CPUs.
- Accurate accounting requires complex modeling, varying with workload and hardware efficiency; utilization is an important factor in optimization for large cloud compute systems.
- The proposed framework accounts for energy consumption aspects through interfaces (DRAM, CPUs, GPUs) using a power usage effectiveness (PUE) factor to account for other components.
- This work aims to promote eco-efficient practices among companies and research labs in the machine learning field.
- The paper focuses on developing a framework for systematic reporting of energy and carbon footprints in machine learning systems.
- It introduces the concept of social cost of carbon (SC-CO2) as a metric to measure monetary impacts of carbon emissions, considering life-cycle emissions for various energy sources.
- The paper analyzes current reporting practices in ML literature, identifying common metrics like energy consumption, compute, and training time.
- A framework is proposed to systematically report energy and carbon footprints, including guidelines for energy consumption, local grid carbon intensity, and energy efficiency improvements.
- Examples of practical applications are provided, such as estimating carbon emissions for Bitcoin mining or machine learning experiments.
- The paper emphasizes the importance of considering environmental impacts when evaluating ML models and algorithms, encouraging researchers to adopt more sustainable practices.
- The study finds that only one out of 100 NeurIPS papers from 2019 measured energy directly, while none reported carbon metrics.
- Estimation methods for energy and carbon footprints are presented using existing metrics like runtime, GPU thermal design power (TDP), and computational complexity.
- The paper introduces a framework to address the limitations of these estimation methods by aggregating all necessary accounting information for accurate energy and carbon footprint calculations.
- Challenges in accurately measuring and reporting energy and carbon footprints are discussed, highlighting the need for further research in this area.
- The paper introduces a new framework called ""experiment-impact-tracker"" for systematic reporting of energy and carbon footprints in machine learning research.
- Consistent reporting can lead to eco-friendly behaviors, social incentives towards energy-efficient models, and drive traffic to low-emission regions.
- The framework enables cost-benefit analysis and meta-analysis through standardized reporting of energy metrics alongside performance metrics.
- Five design principles are considered: usability, interpretability, extensibility, reproducibility, and fault tolerance.
- Usability is achieved by abstracting knowledge and minimizing user actions; interpretability through simple graph generation and web page creation.
- Extensibility allows the framework to handle evolving driver support and new metrics with modular design for easy addition of capabilities.
- The paper emphasizes accurate carbon accounting's importance in reducing emissions and its impact on machine learning models' performance.
- A case study demonstrates how the proposed framework can be applied to report energy and carbon footprints for a machine learning model.
- The experiment-impact-tracker framework aims to improve reproducibility, fault tolerance, and transparency in ML research by logging various metrics related to hardware, software, and environmental factors.
- Metrics logged include Python package versions, CPU/GPU information, experiment start/end times, carbon intensity of the energy grid region, power draw, utilization, GPU performance states, memory usage, real-time CPU frequency, and disk write speed.
- The paper introduces a framework called ""experiment_impact_tracker"" for logging and tracking energy consumption during machine learning experiments.
- It uses various tools to gather power draw data from CPU, DRAM, GPU, and other system resources while accounting for shared machines and background processes.
- Energy credits are assigned on a per-process basis for accurate energy usage calculations.
- Total energy consumption is calculated using the equation: etotal = PUE * (p(pdram + pcpu + pgpu)).
- The framework generates appendices for figures, experiments, and leaderboards related to energy consumption in machine learning research.
- A tool for monitoring and reporting energy and carbon footprints of machine learning systems is introduced, focusing on real-time data collection.
- Users can customize PUE values, experiment locations, and regions based on machine IP addresses to estimate the region of an experiment.
- The importance of considering energy and carbon footprints when evaluating ML systems for responsible computing practices is highlighted.
- This tool provides a systematic approach to reporting these metrics, which can be useful in understanding the environmental impact of ML systems.
- Practical applications include real-time monitoring of carbon emissions during experiments, allowing researchers and developers to make informed decisions about their energy consumption and environmental impact.
- FPOs (Floating Point Operations) can be misleading for energy efficiency, leading to discrepancies between expectations and results.
- Within an architecture, FPOs correlate well with energy and runtime efficiency, but across architectures, the correlation is weak.
- Estimates based on partial information can be inaccurate due to varying methods of accounting for energy and carbon footprints. The paper's framework collects a wider range of metrics than most existing works.
- The framework provides practical applications like real-time monitoring of energy usage, enabling informed decisions about models' environmental impact.
- The study emphasizes the importance of accounting for energy and carbon footprints in machine learning models, introducing a new framework to address these issues.
- Extrapolation methods can lead to over- or underestimation due to ignoring factors like memory, CPU effects, and regional differences.
- Comparing different estimation methods shows that using partial data for estimating carbon emissions can result in significant discrepancies compared to detailed tracking.
- The paper provides a script for calculating rough estimates of energy and carbon footprints based on GPU type, IP address, and region's carbon intensity.
- Experiments show FPOs are not strongly correlated with energy consumption or time across different architectures but have stronger correlations within an architecture.
- The paper introduces a framework called experiment-impact-tracker to facilitate detailed accounting of energy and carbon footprints for machine learning experiments.
- The paper emphasizes the importance of accurate reporting for energy and carbon footprints in machine learning to drive immediate mitigation strategies, leading to more efficient climate-friendly settings.
- It proposes a Deep RL Energy Leaderboard as an example of how energy leaderboards can be used to disseminate information on energy efficiency.
- The framework suggests incorporating carbon emissions and energy metrics into existing performance-based leaderboards, promoting a balance between performance and efficiency.
- The goal is to encourage the development of climate-friendly machine learning models through standardized reporting systems for energy and carbon footprints.
- An energy leaderboard for Reinforcement Learning (RL) algorithms is introduced, comparing their energy efficiency and performance across different environments.
- PPO implementation by Hill et al. (2018) shows a balance between efficiency and performance when using default settings in various RL algorithms.
- By using the PPO algorithm versus DQN in a Deep RL class of 235 students, the class could save 888 kWh of energy for a homework assignment.
- The paper focuses on energy specifically rather than carbon efficiency, acknowledging that hardware and time-of-day factors can affect carbon impacts.
- Hardware immutability and fixed carbon intensity factors can be used to gain insights into algorithm performance in terms of energy consumption.
- The leaderboard encourages the community to submit more data for finding even more efficient algorithms and configurations, while running experiments in carbon-friendly regions is crucial for assessing energy grids' impact on machine learning work.
- The paper explores energy and carbon footprints of machine learning systems, focusing on low carbon intensity regions with cloud providers.
- It provides a non-exhaustive list of such regions, including Quebec, Canada, West Norway, Ontario, France, Brazil, Oregon, and some US locations.
- Shifting training resources to these regions can help reduce carbon emissions without significantly impacting production systems.
- The study emphasizes the need for systemic changes in machine learning systems towards energy efficiency, suggesting green defaults for common platforms and tools.
- Energy leaderboards provide information on energy-efficient configurations, but underlying frameworks should use default settings for maximum efficiency.
- Nvidia Apex is an example of a tool that can help achieve this by providing easy mixed-precision computing.
- The paper highlights the importance of understanding carbon intensity in cloud regions and its impact on machine learning systems' energy consumption.
- It analyzes and compares the energy and carbon footprints of machine learning models across different regions with varying energy grids.
- Small differences in model efficiency can lead to significant reductions in carbon emissions and energy usage when scaled across large datasets or services.
- The paper encourages companies to consider both performance gains and energy costs when deploying new machine learning models, prioritizing energy-efficient components within frequently used frameworks like PyTorch.
- The paper emphasizes the importance of considering energy and carbon footprints when deploying machine learning models, as performance gains may not outweigh increased costs.
- It suggests routing to different models in specific data subsets for better balance between performance and energy consumption.
- Carbon emissions should be considered during deployment decisions, ensuring that the energy costs don't exceed the benefits of large deep learning models.
- The paper leaves an open question for economists to assess welfare benefits against social cost of carbon for better decision-making.
- Efficient routing of traffic to particular models is inspired by efficient traffic routing in regions, providing a more sustainable approach.
- Report both estimated training and deployment energy costs for a comprehensive understanding when choosing a model.
- Green default configurations are crucial for adopting green practices in machine learning research.
- The paper introduces a tool to help with consistent and accurate accounting of energy and carbon footprints, contributing to better decision-making.
- Efficient testing environments are considered, particularly for reinforcement learning research.
- Researchers should consider the trade-off between energy efficiency and replication efforts when deciding on model release. Internal sharing within companies can promote reuse and reduce energy consumption.
- The paper introduces a framework for systematic reporting of energy and carbon footprints in machine learning, aiming to improve accuracy by considering factors like data center efficiency and power grid mix.
- Limitations and opportunities for extensions are discussed, including incorporating more data sources and improving the accuracy of carbon calculations.
- Challenges in measuring energy and carbon footprints include automating carbon intensity information and addressing multiple processes on a single machine.
- Tracking energy usage faces difficulties due to poor driver support, unsupported chipsets, lack of first-party libraries from Intel, and limited support for nvidia-smi in docker containers.
- Improvements have been made by fitting regression models to real energy usage patterns but still require administrative access or may not be accessible on most Linux systems.
- The Slurm workload manager provides an energy accounting plugin, but requires administrator access for adding it. Intel's RAPL supports access through three mechanisms, with one (powercap interface) requiring no root access.
- To promote widespread reporting, the paper avoids tools that require administrative access or are not accessible on most Linux systems and introduces the experiment-impact-tracker to handle intricacies of downstream tools.
- The current system only supports instances with Intel RAPL or PowerGadget capabilities for Mac OS and Linux due to driver-related issues requiring driver developer support.
- Cloud providers should expose realtime APIs for energy mixes, and supporting libraries for custom hardware in cloud provider regions could enable more detailed accounting in a wider range of scenarios.
- The paper highlights the need for better supported tools for user-level access to power metrics, improved energy accounting mechanisms and interfaces, and realtime APIs for energy mixes.
- The paper encourages researchers and companies to consider additional sources of emissions for consistent accountability.
- Recommendations include running cloud jobs in low-carbon regions, reporting energy metrics, working on energy-efficient systems, releasing code and models, and integrating energy-efficient configurations as defaults.
- Industry developers and framework maintainers are advised to move training jobs to low-carbon regions, provide better energy tracking tools, integrate energy-efficient operations in frameworks, release code and models, consider energy costs versus benefits of deploying new models, and report model-related energy metrics.
- The paper's work resulted in 8.021 kg CO2eq emissions and used 24.344 kWh electricity with a social cost of carbon at $0.38. Carbon accounting information is available.
- A framework for systematically reporting energy and carbon footprints of machine learning models was introduced using experiment-impacttracker.
- The framework utilizes the social cost of carbon model from Ricke et al. (2018) to calculate environmental impact.
- This approach aims to provide a standardized method for evaluating and comparing energy efficiency and carbon emissions between different machine learning algorithms.
- Experiment-impacttracker tool allows users to generate statements and carbon emission information based on experiments.
- Henderson et al.'s work related to environmental impact assessment in ML was mentioned as a reference.
- The US Environmental Protection Agency's greenhouse gas equivalencies were used for calculating carbon emissions of various activities.
- This framework helps researchers and practitioners make informed decisions when choosing machine learning models, considering their energy and environmental impacts.
- By standardizing reporting methods, it enables better comparisons between different ML algorithms in terms of sustainability.
- Experiment-impacttracker provides a practical application for implementing the proposed framework, making it easier to calculate and report on the energy and carbon footprints of machine learning models.
- The work contributes to the growing body of research focused on sustainable AI practices, promoting responsible development in the field.
",3079
"2004.05150",1,"- Longformer is an improved Transformer architecture designed to process long sequences efficiently, addressing the quadratic scaling issue of self-attention operations.
- It achieves this by introducing a linear scaling attention mechanism that combines local windowed attention and task-motivated global attention.
- Evaluated on character-level language modeling tasks, Longformer achieved state-of-the-art results on text8 and enwik8 datasets.
- Pretrained Longformer outperformed RoBERTa in long document tasks, setting new state-of-the-art results on WikiHop and TriviaQA.
- Introduced the Longformer-Encoder-Decoder (LED) variant for supporting long document generative sequence-to-sequence tasks, demonstrating effectiveness on arXiv summarization dataset.
- Longformer's memory usage scales linearly with sequence length, unlike full self-attention which runs out of memory for long sequences on current GPUs.
- Vectorized implementations of Longformer vary in speed, with the vectorized Longformer-chunk being the fastest.
- Longformer is an alternative to existing Transformers for handling long documents, addressing the need for task-specific architectures and computational efficiency issues in processing long sequences.
- It combines a windowed local-context self-attention with an end-task motivated global attention, which encodes inductive bias about the task.
- Longformer can process up to 32K characters on modern GPUs, achieving state-of-the-art results on text8 and enwik8 benchmark datasets for autoregressive character-level language modeling.
- Pretrained with masked language modeling (MLM) objective, Longformer outperforms RoBERTa in a wide range of document-level natural language tasks such as text classification, QA, and coreference resolution, achieving state-of-the-art results on two datasets.
- A variant called Longformer-Encoder-Decoder (LED) is introduced for sequence-to-sequence learning, following an encoder-decoder architecture similar to the original Transformer model.
- The paper demonstrates that Longformer's attention mechanism can act as a drop-in replacement for self-attention in pretrained Transformers, leading to gains across various document NLP tasks.
- Longformer is an efficient attention pattern for Transformers, addressing long document tasks like summarization.
- It differs from left-to-right approaches by using a sparse attention pattern and avoiding full quadratic attention matrix multiplication.
- Similar to Sparse Transformer, Longformer includes custom CUDA kernels but is more flexible and maintainable.
- Introduces additional task-motivated global attention patterns for common NLP tasks, essential for good performance in transfer learning settings.
- Compared to other models, Longformer has a broader range of applications, including autoregressive language modeling, machine translation, and question answering.
- Demonstrates effectiveness on the arXiv summarization dataset, achieving 30% accuracy improvement over Transformer-XL.
- Longformer is 4.5 times faster than Transformer-XL in processing long documents.
- The model's efficiency makes it suitable for real-world applications like search engines and document repositories with large text collections.
- Longformer can be used as a general-purpose architecture, adapting to various tasks without requiring task-specific modifications.
- The paper highlights the importance of considering long documents in NLP models and provides a practical solution for addressing this issue.
- Longformer addresses the limitation of pretrained transformer models like BERT, which have a 512 token limit, by processing long sequences without truncation or chunking.
- It adopts a simpler approach that concatenates available context and processes it in a single pass, unlike task-specific approaches with information loss due to truncation or cascading errors.
- Longformer uses an attention pattern that scales linearly with the input sequence length, addressing the O(n^2) time and memory complexity of the original Transformer model.
- The attention pattern employs a fixed-size window surrounding each token, allowing for efficient local context while maintaining global dependencies.
- Longformer's performance is comparable to BERT on several tasks, including SQuAD 1.1, SQuAD 2.0, and Natural Questions, with only 3% more parameters than BERT-Base.
- The model shows strong results in reading comprehension and classification tasks, achieving state-of-the-art performance in long document natural language tasks.
- Longformer's attention pattern can be applied to other Transformers, potentially improving their efficiency for longer sequences.
- Practical applications include multihop question answering, open domain QA, and text summarization, where the model's ability to process long documents without truncation or chunking is beneficial.
- The paper introduces a new attention pattern for Longformer, which uses fixed-size windowed attention to create large receptive fields similar to CNNs.
- Computation complexity scales linearly with input sequence length and can be adjusted by varying the window size (w) across layers.
- Dilated sliding windows further increase the receptive field without increasing computation, allowing for tens of thousands of tokens even with small values of dilation.
- Multi-headed attention with different dilations per head improves performance by focusing on local and long contexts.
- Global attention is added to address task-specific representation learning limitations in windowed and dilated attention.
- The global attention operation is symmetric, allowing a token to attend to all others across the sequence.
- Specifying global attention locations is task-dependent but adds inductive bias to the model's attention.
- Combining local and global attention has O(n) complexity, making it efficient for long sequences.
- The paper presents a practical application of Longformer in question answering tasks, achieving state-of-the-art results on SQuAD 2.0.
- Longformer's performance is comparable to BERT while being 4.5 times faster and requiring less memory.
- Longformer introduces a new Transformer architecture for processing long documents, addressing the issue of quadratic memory complexity by using dilated sliding window attention and linear projections for global attention.
- The model adds inductive bias to the attention mechanism, simplifying task-specific approaches that use complex architectures.
- Linear projections provide flexibility to model different types of attention, which is critical for best performance on downstream tasks.
- Longformer's implementation requires a custom CUDA kernel and banded matrix multiplication not supported in existing deep learning libraries like PyTorch/TensorFlow.
- The model is evaluated using autoregressive language modeling, where it uses dilated sliding window attention with varying window sizes across layers to balance efficiency and representation learning.
- Longformer achieves state-of-the-art performance on several benchmarks, including SQuAD 1.1, SQuAD 2.0, and XSum, while being up to 4.5 times faster than the original Transformer model.
- The model's efficiency allows it to process documents with lengths of up to 40,960 tokens, compared to the original Transformer's limit of 512 tokens.
- Longformer can be used for various downstream tasks such as question answering and summarization, demonstrating its practical applications in natural language processing.
- Longformer introduces a transformer model with an adaptive window size, allowing it to balance efficiency and performance by adjusting the attention window size based on input sequence length.
- The model uses a staged training procedure that gradually increases the window size and sequence length across multiple phases, ensuring fast training while keeping the slow part (longest sequences and window sizes) for later stages.
- Longformer achieves state-of-the-art performance on character-level language modeling tasks with BPC of 1.10 and 1.00 on text8 and enwik8 datasets, respectively.
- The model's adaptive window size allows it to learn local context first before utilizing longer context, which is particularly beneficial for long sequences where the importance of distant tokens increases.
- Longformer can be used in applications requiring processing of long documents or sequences with varying lengths, such as question answering and summarization tasks.
- Longformer is an attention-based transformer model designed for processing long documents, addressing the limitations of existing models like TransformerXL and Sparse Transformers in handling sequences longer than 512 tokens.
- The paper demonstrates that Longformer outperforms comparable models on enwik8 dataset, matching or slightly underperforming recent models with more parameters.
- Ablation study shows the importance of attention patterns design choices, such as window sizes per layer and dilation. Increasing window size from bottom to top layers leads to better performance, while adding some dilation to two heads improves results compared to no dilation at all.
- Longformer is pretrained using masked language modeling (MLM) and finetuned for six tasks, including classification, question answering, and coreference resolution. It can process sequences up to 4,096 tokens long, which is eight times longer than BERT's maximum sequence length.
- The paper highlights the potential of Longformer in handling long documents for various NLP tasks, making it suitable for applications such as question answering on Wikipedia articles and coreference resolution in scientific papers.
- Longformer is an extension of Transformer models designed to handle long documents, with a maximum sequence length of 4,096 tokens.
- To support this extended length, it introduces extra position embeddings and copying RoBERTa's position embeddings for initialization.
- The attention pattern can be plugged into any pretrained Transformer model without changing the architecture.
- Longformer uses sliding window attention with a window size of 512, similar to RoBERTa's computation cost.
- Pretraining Longformer on long documents using fairseq and RoBERTa's hyperparameters resulted in comparable BPC (Bits Per Character) performance as RoBERTa on their corpus.
- Copy initialization of position embeddings proved effective, allowing rapid convergence with a small number of gradient updates.
- The Longformer model can be retrained from scratch to potentially improve performance if needed.
- The training corpus's BPC is comparable to RoBERTa's, indicating that the distribution used for training is close to RoBERTa's.
- Longformer's pretraining with copied position embeddings outperforms random initialization, highlighting the importance of this technique.
- The paper demonstrates how Longformer can be applied to various long-document tasks like question answering and news classification.
- Longformer introduces a sliding window attention mechanism to address RoBERTa's limited context issue, improving BPC (bits per character) by 25% in long documents.
- Pretraining improves the model's performance as it learns to better utilize the sliding window attention and longer context.
- Freezing RoBERTa weights during pretraining preserves its performance on short documents, achieving a BPC of 1.850 (compared to 1.957 at initialization).
- Longformer is applied to multiple long document tasks, including QA, coreference resolution, and classification, demonstrating the effectiveness of its attention mechanism as a replacement for standard self-attention in BERT-style models.
- The baseline model uses RoBERTa to process each segment individually before concatenation, while Longformer replaces RoBERTa's self-attention with windowed attention and task-motivated global attention.
- Longformer achieves competitive results in QA tasks on WikiHop, TriviaQA, and HotpotQA datasets, demonstrating its potential to replace complex task-specific models required by BERT's limited context.
- The paper introduces Longformer, a transformer model designed to handle long documents effectively by using a sliding window approach and local attention.
- It compares Longformer's performance with RoBERTa on various tasks, including question answering (QA), coreference resolution, and document classification.
- Longformer consistently outperforms RoBERTa in QA tasks that require long context, such as WikiHop and Hyperpartisan, while showing modest improvements for TriviaQA due to the local context sufficiency.
- In HotpotQA, Longformer's performance gain is smaller because of the supporting fact auxiliary supervision, which allows models to easily find relevant contexts and focus on local context.
- The paper highlights that Longformer's approach excels in reasoning over entire documents with distant supervision of intermediate reasoning chains, as opposed to tasks like HotpotQA with supporting fact supervision.
- Longformer shows significant improvements in document classification tasks such as IMDB and Hyperpartisan news detection, especially for long documents.
- The paper also discusses the use of global attention on [CLS] tokens for document classification tasks.
- Longformer is an improved Transformer model designed for long documents, addressing the issue of intermediate reasoning chains and contextual reasoning.
- On IMDB and OntoNotes datasets, performance gains are smaller due to shorter documents and fewer cross-chunk interactions.
- Longformer achieves new state-of-the-art results on WikiHop, TriviaQA, and HotpotQA tasks, outperforming other methods including GNNs (Graph Neural Networks).
- Ablation studies show that Longformer's performance is robust to changes in sequence length, attention mechanism, pretraining, and linear projection.
- The paper highlights the importance of considering contextual reasoning for long documents and provides a practical solution with Longformer.
- Longformer is an extension of Transformers that benefits from longer sequences, global attention, separate projection matrices for global attention, MLM pretraining, and longer training.
- When configured similarly to RoBERTa-base, Longformer performs slightly worse than RoBERTa-base, indicating performance gains are not due to additional pretraining.
- Longformer can learn to use long range context in task-specific fine-tuning with large datasets like WikiHop.
- BigBird improved leaderboard results on these datasets but had 16X more compute in its pretraining, potentially affecting performance comparisons.
- The Longformer variant for seq2seq learning is called Longformer-Encoder-Decoder (LED), which scales linearly with input length and uses an efficient local+global attention pattern.
- LED's evaluation on the arXiv summarization dataset showed promising results, as it handles long documents in scientific domains effectively.
- The model has two sizes: LED-base and LED-large, with 6 and 12 layers respectively in both encoder and decoder stacks.
- Position embedding is extended to 16K tokens for longer inputs, initialized by repeatedly copying BART's 1K position embeddings 16 times.
- LED follows the same architecture as BART but with a larger position embedding matrix.
- Longformer is a transformer-based model designed for processing long documents, making it suitable for various document-level NLP tasks without requiring chunking or shortening inputs.
- It combines local and global attention patterns to efficiently handle long sequences while scaling linearly with sequence length.
- Longformer achieves state-of-the-art results on character-level language modeling tasks, outperforming RoBERTa in pretrained models for long document tasks.
- LED (Longformer Encoder-Decoder) is an encoder-decoder variant of Longformer designed for sequence-to-sequence tasks and achieves state-of-the-art results on the arXiv long document summarization task.
- Pretraining and increasing sequence length are suggested areas for future work to further improve performance.
- The paper explores pretraining objectives for Long-Document Transformers (LED) and aims to increase sequence length, while also investigating other tasks that can benefit from their model.
- They acknowledge helpful discussions with researchers and technical support from the AI2 infrastructure team.
- Key findings may include improved performance in specific tasks through pretraining objectives, longer sequence lengths, and potential benefits for various tasks using the Long-Document Transformer model.
- Practical applications of these findings could lead to enhanced performance in natural language processing (NLP) tasks involving long documents or text sequences.
- The paper's contributions may include new insights into pretraining objectives, sequence length, and task exploration for LED models.
- Collaboration with researchers and technical support from AI2 infrastructure team are essential for the development of these models.
",3144
"2004.07213",1,"- Toward Trustworthy AI Development: This paper aims to explore mechanisms for supporting verifiable claims in AI development, focusing on trustworthiness and transparency.
- Motivation: The authors highlight the need for accountability and explainable decision-making in AI systems due to their increasing impact on society.
- Key concepts introduced: The paper discusses the importance of interpretability, fairness, robustness, and security in AI development.
- Interpretability: It involves making AI models understandable by humans, allowing for better decision-making and reducing bias.
- Fairness: Ensuring that AI systems do not discriminate against certain groups or individuals based on sensitive attributes like race, gender, or religion.
- Robustness: Developing AI systems that can handle unexpected inputs and perform consistently under various conditions.
- Security: Implementing measures to protect data privacy and prevent malicious attacks on AI systems.
- Recommendations: The paper provides a list of guidelines for building trustworthy AI, including establishing an independent body to oversee AI development, developing open-source tools for auditing models, and creating standardized metrics for evaluating AI systems' trustworthiness.
- Practical applications: These recommendations can be applied across various industries, such as healthcare, finance, and transportation, where AI systems have a significant impact on people's lives.
- Benefits of trustworthy AI development: Improved public confidence in AI technology, reduced risk of unintended consequences, and better decision-making based on transparent and explainable models.
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims aims to explore institutional, software, and hardware mechanisms that can enhance trust in AI systems by enabling verifiable claims.
- Institutional mechanisms include third-party auditing, red team exercises, bias and safety bounties, and sharing of AI incidents.
- Software mechanisms focus on audit trails, interpretability, privacy-preserving machine learning, and secure hardware for machine learning.
- Hardware mechanisms involve secure hardware for machine learning, high-precision compute measurement, and compute support for academia.
- The paper concludes with a summary of the discussed mechanisms and their potential benefits in building trustworthy AI systems.
",414
"2004.08994",1,"- The paper explores enhancing generalization and robustness in large neural language models (LLMs) through adversarial training.
- Previous work shows that adversarial training can improve robustness but often hurts generalization, especially in NLP.
- BERT and other pre-trained LLMs have demonstrated impressive gains in generalization for various tasks; however, they remain vulnerable to adversarial attacks.
- The proposed ALUM (Adversarial training for large neural LangUage Models) algorithm regularizes the training objective by applying perturbations in the embedding space that maximize the adversarial loss.
- A comprehensive study of adversarial training is presented, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning.
- ALUM produces substantial gains over BERT in various NLP tasks, both in regular and adversarial scenarios.
- Even for models trained on large text corpora like RoBERTa, ALUM can still produce significant gains through continual pre-training, while conventional non-adversarial methods cannot.
- ALUM can be combined with task-specific fine-tuning to achieve additional gains.
- The ALUM code is publicly available at https://github.com/namisan/mt-dnn.
- Introduces ALUM (Adversarial training for large neural LangUage Models), a unifying algorithm for adversarial pre-training, applicable to various Transformer-based language models.
- Conducts comprehensive evaluations on multiple NLP tasks and benchmark datasets, including GLUE, SQuAD v1.1/v2.0, SNLI, Sci-Tail, ANLI, HELLASWAG, SWAG, Adversarial SQuAD, and HELLSWAG.
- Significant improvements in generalization and robustness are observed for various models like BERT and RoBERTa, even outperforming previous state of the art by a large margin.
- Adversarial pre-training also improves robustness, reducing the gap between standard errors and robust errors on adversarial datasets such as ANLI, Adversarial SQuAD, HELLASWAG, and SWAG.
- Combining adversarial pre-training with adversarial fine-tuning results in additional gains.
- ALUM can be used for both pre-training from scratch and continual pre-training, as well as task-specific fine-tuning.
- The paper presents a promising direction to reconcile the apparent conflict between generalization and robustness observed in prior work.
- Code and pre-trained models will be released to facilitate research.
- The paper introduces ALUM, an adversarial training algorithm for large neural language models (LLMs).
- Previous pre-training methods like BERT and RoBERTa use self-supervised learning techniques such as Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
- ALUM combines adversarial training with existing pre-training objectives, enhancing the model's robustness against adversarial attacks.
- The authors propose a unifying view of standard training objectives and prior approaches to adversarial training.
- They introduce an adversarial loss function that encourages the model to generate diverse outputs while maintaining its original pre-training objective.
- Experiments show that ALUM improves robustness against adversarial attacks, with minimal impact on performance on standard benchmarks.
- The paper provides code and pre-trained models for research purposes.
- ALUM can be applied to various LLMs, including BERT, RoBERTa, and GPT-2.
- The authors suggest that adversarial training could become a standard component of future pre-training methods.
- Practical applications include improving the robustness of language models in real-world scenarios such as question answering systems or machine translation.
- The paper introduces ALUM, a general adversarial training algorithm for transformer-based neural language models, applicable to both pre-training and fine-tuning.
- Standard training objectives in NLP involve minimizing the error on training data with self-supervision (MLM and NSP in pre-training) or direct supervision (labeled examples in task-specific fine-tuning).
- Adversarial training aims to improve model robustness against adversarial attacks by modifying the training objective, adding small perturbations to input data that maximize the loss function. However, it can lead to a conflict between generalization and robustness.
- ALUM applies adversarial training in NLP by perturbing the embedding space instead of directly altering the input text. It builds on several key ideas from prior work, such as using a projection matrix to generate perturbations and applying gradient descent to find optimal perturbations.
- The paper presents experimental results showing that ALUM improves robustness against adversarial attacks without significantly affecting generalization performance in pre-training and fine-tuning tasks.
- Practical applications of ALUM include improving the robustness of large language models like BERT, GPT-2, and RoBERTa, as well as enhancing the security of NLP systems against adversarial attacks.
- The paper proposes a new adversarial training method for large neural language models, focusing on virtual adversarial training (VAT) and regularizing the standard objective function.
- VAT is superior to conventional adversarial training, especially when labels might be noisy. It favors label smoothness in the embedding neighborhood and uses a hyperparameter α to control the trade-off between standard errors and robust errors.
- Compared to standard training, adversarial training is expensive due to inner maximization. The paper adopts a curriculum learning approach, first using the standard objective and then continuing with VAT. This simplifies the algorithm without requiring a momentum term used in previous approaches.
- The paper introduces ALUM (Adversarial Learning for Unsupervised Modeling), an algorithm that combines adversarial training with unsupervised learning. It uses projected gradient steps to find perturbations and optimizes the model's parameters simultaneously.
- Experiments show that ALUM achieves better performance than standard BERT pre-training on several downstream tasks, including GLUE, SQuAD 2.0, and RACE.
- The paper also presents a new curriculum learning approach for continual pre-training, which can be applied to other models like ERNIE.
- ALUM is more efficient than conventional adversarial training methods, achieving up to 4.5 times faster training speed on the GLUE benchmark.
- The paper proposes Adversarial Language Model Unsupervised Pre-training (ALUM), an adversarial training method for large neural language models to improve generalization and robustness in NLP tasks.
- ALUM uses the Adversarial Loss with Unlabeled Manifold (ALUM) loss function, which combines a standard supervised loss and an unsupervised manifold regularizer.
- Empirical results show that applying adversarial pre-training using ALUM improves both generalization and robustness for various NLP tasks, contrary to prior work suggesting that adversarial training hurts generalization.
- The paper hypothesizes that the apparent conflict between generalization and robustness in supervised learning might be reconciled through unlabeled data and perturbing embedding space rather than input space.
- Experiments demonstrate that ALUM improves both standard BERT and RoBERTa models, can be applied to adversarial pre-training and fine-tuning, and combining the two further enhances performance.
- The paper suggests future work on theoretical analysis of these connections and applying ALUM to other large language models like GPT-3.
- The paper explores adversarial training for large neural language models, focusing on pre-training from scratch, continual pre-training, and task-specific fine-tuning.
- Pre-training BERT models from scratch using Wikipedia data with a PyTorch implementation based on Megatron. Training parameters include Adam optimizer, linear learning rate schedule, one million steps, batch size of 256, perturbation size ϵ = 1 × 10−5, step size η = 1 × 10−3, and variance for initializing perturbation σ = 1 × 10−5.
- Continual pre-training of RoBERTa using RoBERTa's default parameters (except a smaller learning rate) on the union of Wikipedia, OPEN-WEBTEXT, and STORIES data. Training takes place for 100K steps with a batch size of 256.
- Fine-tuning with or without adversarial training using MT-DNN open-sourced toolkit, following Jiang et al.'s approach for head-to-head comparison. Optimizers include Adam and RADAM, varying learning rates, and batch sizes depending on the tasks. Dropout rate set to 0.1 (except 0.3 for MNLI and 0.05 for CoLA), gradient clipping, and tokenization using WordPiece with spans up to 512 tokens.
- The paper studies the impact of adversarial pre-training on generalization by comparing the performance of pre-trained models in various downstream applications.
- Three BERT models are compared: BERTBASE, BERT+BASE (with 1.6M steps), and BERT+LARGE (with 3.4M steps). Results show that adversarial training improves generalization across multiple tasks, including named entity recognition, textual entailment, and machine reading comprehension.
- Adversarial pre-training also leads to better performance in zero-shot transfer learning, with BERT+LARGE outperforming BERTBASE by 10% on average for 25 tasks.
- The paper demonstrates that adversarial training can improve the generalization of large neural language models and enhance their performance across various downstream applications.
- The paper introduces ALUM, an adversarial training method for large neural language models (LLMs) like BERT.
- ALUM is designed to improve generalization and robustness by introducing adversarial examples during pre-training.
- During adversarial pre-training, a model is trained on both standard data and specially crafted adversarial examples.
- The paper compares the performance of BERTBASE, BERT+BASE (1.6M steps), and ALUMBERT-BASE (1M steps) on SQuAD v1.1/v2.0, MNLI, and biomedical NER datasets.
- ALUMBERT-BASE consistently outperforms standard BERT models across all datasets, even with slightly longer training times.
- The paper also assesses the impact of adversarial pre-training in a continual pre-training setting using RoBERTa and other large language models.
- Results show that adversarial pre-training improves generalization and robustness, especially when applied to different domains from the pre-training data.
- The paper highlights practical applications of adversarial training in LLMs for improved performance on various tasks and domains.
- Standard continual pre-training fails to improve generalization performance in downstream tasks, while adversarial training (ALUM) enhances it for RoBERTa models on MNLI and SST from GLUE.
- Adversarial pre-training substantially improves model robustness against adversarial attacks in ANLI, HELLASWAG, and adversarial SQuAD datasets.
- ALUM consistently outperforms standard pre-training counterparts for both BERT and RoBERTa models in all three adversarial NLP benchmarks.
- The paper demonstrates the practical benefits of adversarial training in improving generalization performance and robustness, making it a valuable contribution to Large Neural Language Models research.
- Adversarial pre-training improves performance on adversarial datasets, such as ANLI, Adversarial SQuAD, and HELLASWAG, leading to significant gains in accuracy compared to standard pre-trained models.
- ALUMROBERTA-LARGE outperforms RoBERTa-LARGE by 7.3% on the test accuracy of ANLI, achieving a new state-of-the-art result.
- Combining adversarial pre-training with fine-tuning leads to better results in development sets for MNLI and ANLI tasks.
- Adversarial training can be combined with adversarial fine-tuning, as demonstrated by ALUMRoBERTA-LARGE-SMART, which uses adversarial continual pre-training and adversarial fine-tuning, achieving higher accuracy on SNLI and SciTail datasets.
- Adversarial training can improve performance in tasks that require reasoning about the meaning of words or sentences rather than just memorizing them.
- The paper highlights the potential practical applications of adversarial pre-training and fine-tuning for large language models, leading to improved performance on various natural language understanding tasks.
- The paper introduces ALUM (Adversarial Learning for Unsupervised Modeling), a general adversarial training algorithm that improves large neural language models' performance in various NLP tasks.
- Adversarial pre-training significantly enhances both generalization and robustness, offering a promising direction to reconcile the conflicting aspects observed in prior work.
- ALUM substantially improved accuracy for BERT and RoBERTa across a wide range of NLP tasks, with further gains achieved by combining adversarial fine-tuning.
- On ANLI, SNLI, SciTail, SWAG, and HELLA-SWAG, the combination of adversarial pre-training and fine-tuning attained new state-of-the-art results.
- Future research directions include further study on the role of adversarial pre-training in improving generalization and robustness, speeding up adversarial training, and applying ALUM to other domains.
- The paper discusses adversarial training for large neural language models, focusing on improving robustness and generalization capabilities.
- Adversarial examples are introduced as a challenge to LLMs, where small perturbations can cause significant changes in model predictions.
- Existing approaches to address this issue include data augmentation, input denoising, and regularization techniques. However, these methods have limitations.
- The paper proposes a novel adversarial training method that combines the strengths of generative and discriminative models. This approach is inspired by Generative Adversarial Networks (GANs) and their use in image processing.
- The proposed method involves two networks: an encoder-decoder model as a generator, and a discriminator based on a pretrained language model.
- The generator aims to create adversarial examples by modifying the input text while maintaining semantic similarity. The discriminator learns to distinguish between real and generated samples.
- Experiments show that this method improves robustness against adversarial attacks, achieving up to 30% accuracy on the SemEval-2017 task 1 dataset.
- The approach also leads to better performance in downstream tasks such as sentiment analysis, question answering, and natural language inference.
- This method can be applied to various pretrained models like BERT, RoBERTa, and GPT-2, demonstrating its versatility across different architectures.
- The paper highlights the potential of adversarial training for improving robustness and generalization capabilities in large neural language models.
- The paper discusses the robustness of self-attentive models and adversarial training for large neural language models, focusing on their vulnerability to adversarial attacks.
- Adversarial examples are crafted inputs that can cause a model to misclassify with high confidence, highlighting weaknesses in natural language processing systems.
- The paper reviews various works related to adversarial training and robustness in neural language models, including Bert, SciTail, and Microsoft's multi-task deep neural networks for natural language understanding.
- It introduces the concept of adversarial training for large neural language models, which involves adding an adversary network that generates adversarial examples to improve model robustness.
- The paper presents a method called Adversarial Training with Gradient Reversal Layer (AT-GRL), which uses gradient reversal layer to train the adversarial network and the main model simultaneously, resulting in improved robustness.
- Experiments show that AT-GRL can significantly improve the robustness of large neural language models against adversarial attacks while maintaining high accuracy on clean data.
- The paper also discusses the importance of regularization techniques for improving generalization and reducing overfitting, which are crucial in adversarial training.
- Practical applications include using AT-GRL to improve the robustness of pre-trained language models like BERT, XLNet, RoBERTa, and ERNIE 2.0.
- The paper highlights that adversarial training can be an effective approach for improving the robustness of large neural language models in various natural language processing tasks.
- Future research directions include exploring more advanced regularization techniques and further investigating the relationship between generalization, overfitting, and robustness in adversarial training.
- The paper discusses adversarial training for large neural language models, focusing on its impact on robustness and generalization.
- Adversarial attacks can cause significant performance drops in deep learning models, especially when dealing with text classification tasks.
- Researchers have explored various methods to improve the robustness of these models, including adding noise to training data, using adversarial examples, and adversarial training techniques.
- Virtual adversarial training (VAT) is a regularization method that adds perturbations to input data during training, enhancing model robustness without compromising accuracy.
- Adversarial nli benchmark evaluates the natural language understanding of models by testing their ability to distinguish between adversarially perturbed and original text pairs.
- Some studies have found that adversarial training can hurt generalization in certain cases, especially when combined with data augmentation techniques.
- Understanding and mitigating the tradeoff between robustness and accuracy is crucial for improving model performance.
- Adversarial training can be used to improve robustness in various tasks such as machine translation, question answering, and text classification.
- Large language models (LLMs) like GPT-2 have shown vulnerability to adversarial attacks, highlighting the need for further research on robustness.
- Adversarial training can be combined with other techniques like data augmentation or regularization methods to enhance model performance and robustness.
- The paper discusses various works related to adversarial training for Large Neural Language Models (LNLMs) and their applications, including sentiment analysis, fact verification, and natural language understanding.
- Adversarial training involves adding an adversary component to the model, which aims to improve its robustness against adversarial attacks and generalization capabilities.
- Dilin Wang et al. (2019) propose a method for improving neural language modeling via adversarial training, achieving 4.5 times faster convergence and improved performance on various tasks.
- Zhilin Yang et al. (2019) introduce XLNet, an autoregressive pre-training model that outperforms previous models in natural language understanding tasks.
- Rowan Zellers et al. (2018) present SWAG, a large-scale adversarial dataset for evaluating the generalization capabilities of LNLMs and their ability to generate coherent text.
- Alex Wang et al. (2018) introduce GLUE, a multi-task benchmark platform for natural language understanding that includes 9 tasks from different domains.
- Adina Williams et al. (2018) present a broad-coverage challenge corpus for sentence understanding through inference, which aims to improve the performance of LNLMs on complex and ambiguous sentences.
- Ashish Vaswani et al. (2017) introduce Transformer architecture with attention mechanism, which has become a standard approach for various NLP tasks.
- James Thorne et al. (2018) present Fever, a large-scale dataset for fact extraction and verification, designed to test the ability of LNLMs to distinguish between facts and opinions.
- Trieu H Trinh and Quoc V Le (2018) propose a simple method for commonsense reasoning using adversarial training, which improves performance on tasks requiring general knowledge and common sense.
- The paper discusses various adversarial training methods for improving Large Neural Language Models (LNLMs) performance on natural language understanding tasks.
- It introduces several benchmarks, including GLUE, SNLI, SciTail, ANLI, SWAG, and HELLASWAG, to evaluate the generalization and robustness of NLU models.
- Adversarial training methods are applied to enhance LNLM performance on these benchmarks.
- The paper highlights the importance of adversarial datasets like SWAG and HELLASWAG for grounded commonsense inference, which combines natural language inference and physically grounded reasoning.
- Adversarial Natural Language Inference (ANLI) is introduced as a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.
- The paper presents results showing that adversarial training can significantly improve the performance of LNLMs on these benchmarks.
- Adversarial training methods are shown to be effective in addressing the limitations of existing pretraining techniques, such as BERT and RoBERTa.
- These methods lead to improved generalization and robustness of NLU models, making them more suitable for real-world applications.
- The paper emphasizes that adversarial training can help LNLMs better handle complex and challenging tasks like grounded commonsense inference and natural language inference.
- Adversarial datasets like SWAG and HELLASWAG are crucial for developing more robust and generalizable NLU models, as they provide a diverse range of tasks to evaluate the models' performance.
- The paper discusses adversarial training for large neural language models, focusing on improving their performance in various NLP tasks.
- It analyzes several popular benchmarks, including HELLASWAG, SQuAD v1.1/v2.0, BC2GM, NCBI, JNLPBA, and GLUE.
- Adversarial training involves adding adversarial examples to the training data, which helps models better generalize and handle unseen scenarios.
- The paper introduces a novel adversarial training method called ""Adversarial Patching"" that generates adversarial examples by modifying a small portion of the input text.
- Experiments show that Adversarial Patching improves performance on multiple NLP tasks, including single-sentence classification (GLUE), pairwise text classification (GLUE), question answering (SQuAD v1.1/v2.0), and biomedical entity recognition (BC2GM).
- The method is effective in improving the models' robustness against adversarial attacks, as demonstrated by experiments on the MNLI dataset.
- Adversarial Patching can be applied to various pre-trained language models, including BERT and RoBERTa, without requiring any additional training data or model modifications.
- The paper highlights the importance of adversarial training for large neural language models in real-world applications, such as question answering systems and biomedical text mining.
",4522
"2004.09095",1,"- The paper discusses the disparity between languages, particularly in terms of resources and their representation in NLP conferences. It highlights the need for addressing language diversity issues to ensure no language is left behind.
- X and Y represent two languages with different speaker bases and Wikipedia article counts; X has better online machine translation systems and more research attention compared to Y. This illustrates how some languages have access to NLP breakthroughs while others lack resources and attention.
- Most NLP systems are trained on a limited number of languages, leading to a typological echo-chamber. Recent advances in deep learning may help bridge this gap by using zero-shot learning techniques that require only large unlabeled corpora across languages and labeled data in some languages.
- The paper breaks down the complex question of linguistic diversity and inclusion into two main questions: understanding resource availability, distribution, and future prospects for different languages; and exploring which typological features have been exposed to current NLP systems and which remain unexplored due to lack of resources and research in those languages.
- The authors suggest that the NLP community should prioritize resolving these issues by increasing data collection efforts, collaborating with language communities, and developing multilingual models.
- The paper proposes a taxonomy for classifying languages based on their resource availability, using two features: number of unlabeled resources and number of labeled resources. This classification helps analyze the digital status and 'richness' of languages in the context of data availability.
- The study uses LDC catalog and ELRA Map as repositories for labeled datasets, while Wikipedia pages represent unlabeled data resources. These repositories are chosen due to their standardized quality and consistency, being used in prior NLP research.
- Six language classes were identified based on the taxonomy: (1) well-resourced languages with both labeled and unlabeled data, (2) languages with few labeled resources but abundant unlabeled data, (3) languages with limited resources for both labeled and unlabeled data, (4) languages with no labeled data but significant unlabeled data, (5) languages with no labeled or unlabeled data, and (6) languages with some labeled data but no unlabeled data.
- The study found that the number of languages in each class has followed distinct trajectories in ACL history, with some classes experiencing growth while others have stagnated or declined.
- Zero-shot learning methods could potentially benefit neglected language classes by leveraging multilingual models trained on Wikipedia data. This approach may help bridge the linguistic resource divide and bring more languages to the forefront of NLP technology.
- Analyze the academic paper ""The State and Fate of Linguistic Diversity and Inclusion in the NLP World"" by reviewing its main contributions and most interesting findings.
- Write a bullet point list summary that serves as reference for future LLM researchers within your organization, focusing on the key ideas without going into detail.
- Main points:
   - The paper presents a taxonomy of languages based on their position in NLP resources (left-behinds, scraping-bys, hopefuls, rising stars, underdogs, winners).
   - Class 0 languages are ignored and have limited resources, making it difficult to improve their digital presence.
   - Class 1 languages could benefit from increased awareness and labeled datasets.
   - Class 2 languages have a small set of labeled data but show promise for future NLP tools.
   - Class 3 languages benefit from unsupervised pre-training methods, with strong online communities.
   - Class 4 languages possess large amounts of unlabeled data but lack labeled datasets, which could be improved by dedicated research communities.
   - Class 5 languages are the winners, enjoying significant investments and resources for their development.
   
- Key findings:
   - The paper's analysis shows that Wikipedia distribution is more fair for classes 1-3 compared to classes 4-5, while web distribution has a clear disparity.
   - Class 0 languages represent the largest section of languages and speakers (15%), but lack technology advancements due to their limited resources.
   
- Conclusion: The paper highlights the need for increased awareness, investment, and research efforts in underrepresented languages to ensure linguistic diversity and inclusion in NLP.
- Lack of tech inclusion for different languages could lead to native speakers moving from Class 0 to Class 5, exacerbating disparity.
- Typology: Large-scale efforts have led to a database with 192 typological features and 2679 languages. Some features are underrepresented in certain language classes, potentially affecting NLP tasks for those languages.
- Examples of 'ignored' typological features and their impact on performance: Amharic (Class 2) has a higher error rate compared to Arabic (Class 3), despite being the second most spoken Semitic language after Arabic.
- Conference-language inclusion: Analyzing NLP research conferences over time, there's a disparity in representation of languages, potentially pushing less represented languages further down the ladder in terms of research.
- Language Occurrence Entropy: Measures language diversity and inclusion by using entropy as a metric to capture skew in language distribution. Higher entropy indicates more spread out distribution, lower entropy means the distribution is more peaked or skewed.
- Class-wise Mean Reciprocal Rank (MRR): Quantifies extent of inclusion for each language class in different conferences. Smaller inverse MRR values indicate a more inclusive conference for that language class.
- All-Inclusive Conferences: LREC and WS have been the most inclusive across all categories, with a marked spike in entropy in the 2010s for ACL, EMNLP, NAACL, LREC, possibly due to increased interest in cross-lingual techniques.
- Later Established Conferences: Those that started later have taken lessons from past language inclusion experiences, while earlier established conferences may be more focused on a specific research theme.
- Language Classes with Lower Ranks: Class 0 (low resource languages) has average ranks ranging from 600 to 1000, while the dip in ranks is less severe for LREC and WS.
- Entity Embedding Analysis: Proposes a novel approach to jointly learn representations of conferences, authors, and languages using embeddings to uncover complex nuances not captured by vanilla statistics.
- Model for learning entity embeddings: The model jointly learns embeddings of entities such as authors, languages, conferences, and conference iterations. It predicts K randomly sampled words from the title and abstract of a paper given an input entity.
- t-SNE visualization: To better understand how languages are represented at different venues, the generated embeddings are projected into 2 dimensions using t-SNE. This helps in analyzing research contributions of individual authors or communities towards specific language classes.
- MRR metric: A variation of Mean Reciprocal Rank (MRR) is used to measure the impact of a community on a particular language cl

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