Awesome papers, datasets and projects about the study of large language models like GPT-3, GPT-3.5, ChatGPT, GPT-4, etc.

• A Survey on In-context Learning (ARXIV, 2023) [paper]

• A SURVEY ON GPT-3 (ARXIV, 2023) [paper]

• A Survey of Large Language Models (ARXIV, 2023) [paper]

• Tree of Thoughts: Deliberate Problem Solving with Large Language Models (ARXIV, 2023) [paper][code]

• GPT-4 Technical Report (OPENAI, 2023) [paper]

• ReAct: Synergizing Reasoning and Acting in Language Models (ICLR, 2023, Notable-top-5%) [paper][code]

• Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning (ICLR, 2023, Notable-top-5%) [paper]

• What learning algorithm is in-context learning? Investigations with linear models (ICLR, 2023, Notable-top-5%) [paper]

• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. (ICLR, 2023) [paper][code]

• Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models. (ARXIV, 2023) [paper][code]

• Toolformer: Language Models Can Teach Themselves to Use Tools. (ARXIV, 2023) [paper][code]

• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback. (ARXIV, 2023) [paper]

• Can GPT-3 Perform Statutory Reasoning? (ARXIV, 2023) [paper]

• How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection (ARXIV, 2023) [paper]

• Large Language Models Can Be Easily Distracted by Irrelevant Context (ARXIX, 2023) [paper]

• Theory of Mind May Have Spontaneously Emerged in Large Language Models (ARXIV, 2023) [paper]

• ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports (ARXIV, 2023) [paper]

• Large Language Models are Zero-Shot Reasoners (Neurips, 2022) [paper]

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models [paper]

• Automatic Chain of Thought Prompting in Large Language Models [paper]

• What can transformers learn in-context? a case study of simple function classes (ARXIV, 2022) [paper][code]

• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (ARXIV, 2022) [paper][code]

• Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (ARXIV, 2022) [paper]

• Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization (PMLR, 2021) [paper]

• GPT Understands, Too (ARXIV, 2021) [paper]

• WebGPT: Browser-assisted question-answering with human feedback （ARXIV, 2021） [paper][code]

• Language Models are Few-shot Learners (Neurips, 2020) [paper]

• Hello-SimpleAI/HC3

• Awesome ChatGPT Prompts