Go to ICML 2024 Workshop LLMs and Cognition homepageAutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
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.