NLP
Learning Transferable Visual Models From Natural Language Supervision
Training Language Models to Follow Instructions with Human Feedback (InstructGPT)
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Self-Rewarding Language Models
REALM: Retrieval-Augmented Language Model Pre-Training
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Improving Language Understanding by Generative Pre-Training
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Large Language Models are Zero-Shot Reasoners
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
ALBERT: A Lite BERT for Self-Supervised Learning of Languange Representations
DistilBERT, a distilled version of BERT: smaller, faster and lighter
DeBERTa: Decoding-Enhanced BERT with Disentangled Attention
RoBERTa: A Robustly Optimized BERT Pretraining Approach
BERT: Bidirectional Encoder Representations from Transformers