6.3k citations · 45.8k across the 278 of their papers we have counts for
5 papers · 1 filter
Learning to Model the World with Language
Jessy Lin, Yuqing Du, Olivia Watkins +4
To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents can learn to e…
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande, Eric Wallace, Charlie Snell +5
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Inst…
Blockwise Parallel Transformer for Large Context Models
Hao Liu, Pieter Abbeel
Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. Howe…
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Tianjun Zhang, Fangchen Liu, Justin Wong +2
Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback. The so-called algorithm, Reinforcement Learni…
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models
Hao Liu, Xinyang Geng, Lisa Lee +4
Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing imp…