How are documents usually evaluated in the simplest form of keyword-based search? According to the length of the documents Based on the number of images and videos contained in the documents By the complexity of language used in the documents ** Based on the presence and frequency of the user-provided keywords
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)? When you want to optimize the model without any instructions When the LLM requires access to the latest data for generating outputs ** When the LLM does not perform well on a task and the data for prompt engineering is too large When the LLM already understands the topics necessary for text generation
In which scenario is soft prompting appropriate compared to other training styles? When the model requires continued pretraining on unlabeled data When the model needs to be adapted to perform well in a domain on which it was not originally trained ** When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training When there is a significant amount of labeled, task-specific data available
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary? Increasing the temperature removes the impact of the most likely word. Decreasing the temperature broadens the distribution, making less likely words more probable. ** Increasing the temperature flattens the distribution, allowing for more varied word choices. Temperature has no effect on probability distribution; it only changes the speed of decoding.
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)? PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning. ** Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
What does accuracy measure in the context of fine-tuning results for a generative model? ** How many predictions the model made correctly out of all the predictions in an evaluation The number of predictions a model makes, regardless of whether they are correct or incorrect The depth of the neural network layers used in the model The proportion of incorrect predictions made by the model during an evaluation
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail? Picking a word based on its position in a sentence structure ** Choosing the word with the highest probability at each step of decoding Selecting a random word from the entire vocabulary at each step Using a weighted random selection based on a modulated distribution
In the simplified workflow for managing and querying vector data, what is the role of indexing? ** To map vectors to a data structure for faster searching, enabling efficient retrieval To categorize vectors based on their originating data type (text, images, audio) To convert vectors into a nonindexed format for easier retrieval To compress vector data for minimized storage usage
When does a chain typically interact with memory in a run within the LangChain framework? Continuously throughout the entire chain execution process Only after the output has been generated Before user input and after chain execution ** After user input but before chain execution, and again after core logic but before output
What do prompt templates use for templating in language model applications? Python's lambda functions Python's class and object structures Python's list comprehension syntax ** Python's str.format syntax
What does a cosine distance of 0 indicate about the relationship between two embeddings? ** They are similar in direction They are unrelated They are completely dissimilar They have the same magnitude
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)? It does not update any weights but restructures the model architecture. ** It selectively updates only a fraction of the model's weights. It updates all the weights of the model uniformly. It increases the training time as compared to Vanilla fine-tuning.
What does the Loss metric indicate about a model's predictions? Loss measures the total number of predictions made by a model. Loss describes the accuracy of the right predictions rather than the incorrect ones. ** Loss is a measure that indicates how wrong the model's predictions are. Loss indicates how good a prediction is, and it should increase as the model improves.
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why are they crucial for language models? Hierarchical relationships; important for structuring database queries ** Semantic relationships; crucial for understanding context and generating precise language Linear relationships; they simplify the modeling process Temporal relationships; necessary for predicting future linguistic trends
How does a presence penalty function in language model generation? It penalizes all tokens equally, regardless of how often they have appeared. It penalizes only tokens that have never appeared in the text before. ** It penalizes a token each time it appears after the first occurrence. It applies a penalty only if the token has appeared more than twice.
How does the structure of vector databases differ from traditional relational databases? A vector database stores data in a linear or tabular format. ** It is based on distances and similarities in a vector space. It uses simple row-based data storage. It is not optimized for high-dimensional spaces.
Why is it challenging to apply diffusion models to text generation? ** Because text representation is categorical unlike images Because text generation does not require complex models Because text is not categorical Because diffusion models can only produce images
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)? When the LLM requires access to the latest data for generating outputs When you want to optimize the model without any instructions ** When the LLM does not perform well on a task and the data for prompt engineering is too large When the LLM already understands the topics necessary for text generation
In which scenario is soft prompting appropriate compared to other training styles? When there is a significant amount of labeled, task-specific data available ** When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training When the model needs to be adapted to perform well in a domain on which it was not originally trained When the model requires continued pretraining on unlabeled data
What is the purpose of Retrievers in LangChain? To break down complex tasks into smaller steps To combine multiple components into a single pipeline ** To retrieve relevant information from knowledge bases To train Large Language Models
What does the RAG Sequence model do in the context of generating a response? It modifies the input query before retrieving relevant documents to ensure a diverse response. ** For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response. It retrieves relevant documents only for the initial part of the query and ignores the rest. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
What is the purpose of Retrieval Augmented Generation (RAG) in text generation? To store text in an external database without using it for generation To generate text based only on the model's internal knowledge without external data ** To generate text using extra information obtained from an external data source To retrieve text from an external source and present it without any modifications
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)? Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive. ** Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
Which LangChain component is responsible for generating the linguistic output in a chatbot system? LangChain Application ** LLMs Vector Stores Document Loaders
How are documents usually evaluated in the simplest form of keyword-based search? Based on the number of images and videos contained in the documents ** Based on the presence and frequency of the user-provided keywords According to the length of the documents By the complexity of language used in the documents
Given the following code block: history = StreamlitChatMessageHistory(key="chat_messages") memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory? A given StreamlitChatMessageHistory will NOT be persisted. A given StreamlitChatMessageHistory will not be shared across user sessions. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key. ** StreamlitChatMessageHistory can be used in any type of LLM application.
Which statement is true about string prompt templates and their capability regarding variables? ** They support any number of variables, including the possibility of having none. They can only support a single variable at a time. They are unable to use any variables. They require a minimum of two variables to function properly.
When does a chain typically interact with memory in a run within the LangChain framework? Only after the output has been generated Before user input and after chain execution Continuously throughout the entire chain execution process ** After user input but before chain execution, and again after core logic but before output
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)? Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy. ** Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
What is LangChain? A Ruby library for text generation ** A Python library for building applications with Large Language Models A JavaScript library for natural language processing A Java library for text summarization
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used? Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data. Soft prompting and continuous pretraining are both methods that require no modification to the original parameters of the model. Parameter Efficient Fine Tuning and Soft prompting modify all parameters of the model using unlabeled data. ** Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
What does in-context learning in Large Language Models involve? Pretraining the model on a specific domain Training the model using reinforcement learning Adding more layers to the model ** Conditioning the model with task-specific instructions or demonstrations
What does the term "hallucination" refer to in the context of Language Large Models (LLMs)? The process by which the model visualizes and describes images in detail The model's ability to generate imaginative and creative content A technique used to enhance the model's performance on specific tasks ** The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
What is prompt engineering in the context of Large Language Models (LLMs)? ** Iteratively refining the ask to elicit a desired response Training the model on a large data set Adding more layers to the neural network Adjusting the hyperparameters of the model
What is the role of temperature in the decoding process of a Large Language Model (LLM)? ** To adjust the sharpness of probability distribution over vocabulary when selecting the next word To increase the accuracy of the most likely word in the vocabulary To determine the number of words to generate in a single decoding step To decide to which part of speech the next word should belong
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)? It allows the LLM to access a larger data set. It significantly reduces the latency for each model request. It eliminates the need for any training or computational resources. ** It provides examples in the prompt to guide the LLM to better performance with no training cost.
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks? GPUs are shared with other customers to maximize resource utilization. GPUs are used exclusively for storing large data sets, not for computation. Each customer's GPUs are connected via a public Internet network for ease of access. ** The GPUs allocated for a customer’s generative AI tasks are isolated from other GPUs.
What happens if a period (.) is used as a stop sequence in text generation? The model generates additional sentences to complete the paragraph. ** The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher. The model ignores periods and continues generating text until it reaches the token limit. The model stops generating text after it reaches the end of the current paragraph.
What is the purpose of frequency penalties in language model outputs? To reward the tokens that have never appeared in the text ** To penalize tokens that have already appeared, based on the number of times they have been used To randomly penalize some tokens to increase the diversity of the text To ensure that tokens that appear frequently are used more often
What is the purpose of embeddings in natural language processing? To increase the complexity and size of text data To translate text into a different language To compress text data into smaller files for storage ** To create numerical representations of text that capture the meaning and relationships between words or phrases
What is the function of the Generator in a text generation system? To store the generated responses for future use To rank the information based on its relevance to the user's query ** To generate human-like text using the information retrieved and ranked, along with the user's original query To collect user queries and convert them into database search terms
What does the Ranker do in a text generation system? It sources information from databases to use in text generation. It generates the final text based on the user's query. It interacts with the user to understand the query better. ** It evaluates and prioritizes the information retrieved by the Retriever.
What differentiates Semantic search from traditional keyword search? ** It involves understanding the intent and context of the search. It is based on the date and author of the content. It relies solely on matching exact keywords in the content. It depends on the number of times keywords appear in the content.
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)? ** They rely on internal knowledge learned during pretraining on a large text corpus. They use vector databases exclusively to produce answers. They always use an external database for generating responses. They cannot generate responses without fine-tuning.
What do embeddings in Large Language Models (LLMs) represent? The grammatical structure of sentences in the data The color and size of the font in textual data ** The semantic content of data in high-dimensional vectors The frequency of each word or pixel in the data
How are chains traditionally created in LangChain? Declaratively, with no coding required ** Using Python classes, such as LLM Chain and others Exclusively through third-party software integrations By using machine learning algorithms
What is the purpose of memory in the LangChain framework? ** To store various types of data and provide algorithms for summarizing past interactions To retrieve user input and provide real-time output only To perform complex calculations unrelated to user interaction To act as a static database for storing permanent records
How are prompt templates typically designed for language models? ** As predefined recipes that guide the generation of language model prompts To work only with numerical data instead of textual content As complex algorithms that require manual compilation To be used without any modification or customization
What is LECL in the context of LangChain Chains? A programming language used to write documentation for LangChain ** A declarative way to compose chains together using LangChain Expression Language A legacy method for creating chains in LangChain An older Python library for building Large Language Models
What is the function of "Prompts" in the chatbot system? They handle the chatbot's memory and recall abilities. They are responsible for the underlying mechanics of the chatbot. They store the chatbot's linguistic knowledge. ** They are used to initiate and guide the chatbot's responses.
An LLM emits intermediate reasoning steps in responses during what type of prompting? In context learning Soft prompting Least to most ** Chain of thought
What is a characteristic of T Few fine tuning? It updates model weights uniformly It updates a fraction of weights to reduce number of parameters ** It selectively updates weights to reduce compurational load and prevent overfitting It increases training time
For a fine tuning dedicated cluster how many minimum unit hours for 10 days? Hint: Fine-tuning requires 2 unit to run, 10 days multiplied by 24 hours per day multiplied by 2 unit is 480. Hosting cluster is 744. 200 240 744 ** 480
When building a chatbot with an online retail company's internal store policies and a memory of conversation which format? Keyword search based chatbot ** Retrieval augmented generation RAG chatbot A general chatbot with pretrained responses and no external data A default oretrained LLM from OpenAI or Cohere
