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Large Language Models Can Self-Improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou +4
Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improv…
Mind's Eye: Grounded Language Model Reasoning through Simulation
Ruibo Liu, Jason Wei, Shixiang Shane Gu +5
Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the…
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid +2
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specifi…
Human-centric Dialog Training via Offline Reinforcement Learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun +5
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting…