AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
<|reference_start|>AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents: 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.<|reference_end|>
@article{fu2024autoguide:,
    title={AutoGuide: Automated Generation and Selection of State-Aware Guidelines
  for Large Language Model Agents},
    author={Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen
  Logeswaran, Kyunghoon Bae, Honglak Lee},
    journal={arXiv preprint arXiv:2403.08978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08978},
    primaryClass={cs.CL cs.LG}
}