The primary limitation of large language models (LLMs) is their restricted
understanding of the world. This poses significant difficulties for LLM-based
agents, particularly in domains where pre-trained LLMs lack sufficient
knowledge. In this paper, we introduce a novel framework, called AutoGuide,
that bridges the knowledge gap in pre-trained LLMs by leveraging implicit
knowledge in offline experiences. Specifically, AutoGuide effectively extracts
knowledge embedded in offline data by extracting a set of state-aware
guidelines. Importantly, each state-aware guideline is expressed in concise
natural language and follows a conditional structure, clearly describing the
state where it is applicable. As such, the resulting guidelines enable a
principled way to provide helpful knowledge pertinent to an agent's current
decision-making process. We show that our approach outperforms competitive
LLM-based baselines by a large margin in sequential decision-making benchmarks.

通过提取蕴含于离线数据中的知识，AutoGuide 框架在大型语言模型的知识缺失问题上取得了重大突破，并通过提供与代理程序当前决策过程相关的有用知识，在顺序决策基准测试中大幅优于竞争的基于大型语言模型的基线模型。