masta-g3

Cedille: A large autoregressive French language model
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks
Query2doc: Query Expansion with Large Language Models
The Internal State of an LLM Knows When its Lying
Structured information extraction from complex scientific text with fine-tuned large language models
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Large Language Models Encode Clinical Knowledge
PoET: A generative model of protein families as sequences-of-sequences
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

BIGBALLON

n00004475,organism, being
n00005787,benthos
n00006024,heterotroph
n00006484,cell
n00007846,person, individual, someone, somebody, mortal, soul
n00015388,animal, animate_being, beast, brute, creature, fauna
n00017222,plant, flora, plant_life
n00021265,food, nutrient
n00021939,artifact, artefact
n00120010,hop

yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

lirantal

The following creates a container with a mounted volume so it can be used as a sandbox that doesn't expose your local development environment incase of any rogue npm packages that steal your .npmrc token, environment variables and others

Run the following:

davidhcefx

Prettydot - Human readable dot graph language for CFG

• Background
• Usage
• Compilation

Background

[Dot][] is a graph description language in extensions .dot or .gv, and can be read by programs such as Graphviz ([try it online][]). However, it's syntax is too complicated for the goal of generating a simple control-flow graph, and is not very well human readable. Therefore, I defined a new language, prettydot, for this purpose.

taskylizard

Moved to https://api.fmhy.net

IronBlood

Exciting Repos

Disclaimer

These repos are selected from GitHub Explore daily / weekly newsletters based on my interests since 2018. There're tons of exciting / awesome repos on github, here's only part of them selected for my furthur studies, and wish they're useful to you as well. Listed in alphabetical order.

这些的 repo 摘选自从 2018 年起 GitHub Explore 基于我个人兴趣生成的日报或者周报。GitHub 上有非常多优秀的开源仓库，这里仅仅是一小部分我个人未来学习参考的项目，希望它们也能给您带来帮助。仓库按照字母表排序。

Table of Content

planetceres

Restart/reset USB kernel module in Ubuntu 18.04 without rebooting

Restart USB ports after a power overdraw (error -110). Unplug all devices prior to running script.

References:

• https://davidjb.com/blog/2012/06/restartreset-usb-in-ubuntu-12-04-without-rebooting/
• https://ubuntuforums.org/showthread.php?t=1460790&p=9162799#post9162799

Gopi-Durgaprasad

Open domain question and aswers

Open-domain v/s Closed-domain QA

• Open-domain QA: Open-domain question answering system aims at returning an answer in response to the user's question. The returned answer is in the form of short texts rather than a list of relevant documents
• Closed-domain QA: Closed-domain question answering deals with questions under a specific domain (for example, medicine or automotive maintenance)

Open-domain QA

overview

Recent advances in natural language processing have largely built upon the power of unsupervised pre-training, which trains general purpose language representation models using a large amount of text, without human annotations or labels. These pre-trained models, such as BERT and RoBERTa, have been shown to memorize a surprising amount of world knowledge, such as “the birthplace of [France

sebinbabu

show_banner(){
  echo
  echo "
  CQ BASIC LINUX HARDENING
  "
  echo
  echo
}

show_done() {