AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents Dong-Ki Kim 2 Jaekyeom Kim

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.