51 citations · 81 across the 13 of their papers we have counts for
8 papers · 1 filter
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Marwa Abdulhai, Isadora White, Charlie Snell +5
Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed age…
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…
Learning by Distilling Context
Charlie Snell, Dan Klein, Ruiqi Zhong
Language models significantly benefit from context tokens, such as prompts or scratchpads. They perform better when prompted with informative instructions, and they acquire new rea…
Offline RL for Natural Language Generation with Implicit Language Q Learning
Charlie Snell, Ilya Kostrikov, Yi Su +2
Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by…
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL
Ruiqi Zhong, Charlie Snell, Dan Klein +1
Can non-programmers annotate natural language utterances with complex programs that represent their meaning? We introduce APEL, a framework in which non-programmers select among ca…
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Charlie Snell, Mengjiao Yang, Justin Fu +2
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of pro…