AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
Abstract
Recent advances in large language models (LLMs) have empowered AI agents to perform 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. As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process. Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains.
Cite
Text
Fu et al. "AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents." ICML 2024 Workshops: LLMs_and_Cognition, 2024.Markdown
[Fu et al. "AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents." ICML 2024 Workshops: LLMs_and_Cognition, 2024.](https://mlanthology.org/icmlw/2024/fu2024icmlw-autoguide/)BibTeX
@inproceedings{fu2024icmlw-autoguide,
title = {{AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents}},
author = {Fu, Yao and Kim, Dong-Ki and Kim, Jaekyeom and Sohn, Sungryull and Logeswaran, Lajanugen and Bae, Kyunghoon and Lee, Honglak},
booktitle = {ICML 2024 Workshops: LLMs_and_Cognition},
year = {2024},
url = {https://mlanthology.org/icmlw/2024/fu2024icmlw-autoguide/}
}