AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

• URL: http://arxiv.org/abs/2403.08978v2
• Date: Tue, 03 Dec 2024 07:36:47 GMT
• Title: AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
• Authors: Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee,
• Abstract summary: We introduce a novel framework, called AutoGuide, which automatically generates 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.
• Score: 74.17623527375241
• License:
• Abstract: 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.

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