@inproceedings{
fu2024autoguide,
title={AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents},
author={Yao Fu and Dong-Ki Kim and Jaekyeom Kim and Sungryull Sohn and Lajanugen Logeswaran and Kyunghoon Bae and Honglak Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mRIQz8Zd6O}
}
Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perform well in unfamiliar domains like web navigation, where they lack sufficient knowledge, has proven to be difficult with the demonstration-based in-context learning paradigm. In this paper, we introduce a novel framework, called AutoGuide, which addresses this limitation by automatically generating context-aware guidelines from offline experiences. Importantly, each context-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the context where it is applicable. As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process, overcoming the limitations of the conventional demonstration-based learning paradigm. Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains, including real-world web navigation.
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
Yao Fu Dong-Ki Kim Jaekyeom Kim Sungryull Sohn Lajanugen Logeswaran Kyunghoon Bae Honglak Lee