Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic

• URL: http://arxiv.org/abs/2412.09666v1
• Date: Thu, 12 Dec 2024 18:16:46 GMT
• Title: Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic
• Authors: Haoming Li, Zhaoliang Chen, Songyuan Liu, Yiming Lu, Fei Liu,
• Abstract summary: We provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems. Our analysis reveals that although it is difficult for LLMs to generate correct plans out-of-the-box, LLMs are much better at providing feedback signals to intermediate/incomplete solutions.
• Score: 6.687149103409949
• License:
• Abstract: In this work, we provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems. In particular, we examine how LLMs perform when they are used as problem solver, solution verifier, and heuristic guidance to improve intermediate solutions. Our analysis reveals that although it is difficult for LLMs to generate correct plans out-of-the-box, LLMs are much better at providing feedback signals to intermediate/incomplete solutions in the form of comparative heuristic functions. This evaluation framework provides insights into how future work may design better LLM-based tree-search algorithms to solve diverse planning and reasoning problems. We also propose a novel benchmark to evaluate LLM's ability to learn user preferences on the fly, which has wide applications in practical settings.

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