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

• URL: http://arxiv.org/abs/2403.08978v1
• Date: Wed, 13 Mar 2024 22:06:03 GMT
• Title: AutoGuide: Automated Generation and Selection of State-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: AutoGuide bridges the knowledge gap in pre-trained LLMs by leveraging implicit knowledge in offline experiences. We show that our approach outperforms competitive LLM-based baselines by a large margin in sequential decision-making benchmarks.
• Score: 74.17623527375241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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