When is Tree Search Useful for LLM Planning? It Depends on the Discriminator

• URL: http://arxiv.org/abs/2402.10890v2
• Date: Thu, 6 Jun 2024 14:55:40 GMT
• Title: When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
• Authors: Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, Huan Sun,
• Abstract summary: Large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using advanced planning methods.
• Score: 15.75807429396126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using these two methods or a simpler method, re-ranking. Experiments on two tasks, text-to-SQL parsing and mathematical reasoning, show that: (1) advanced planning methods demand discriminators with at least 90% accuracy to achieve significant improvements over re-ranking; (2) current LLMs' discrimination abilities have not met the needs of advanced planning methods to achieve such improvements; (3) with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency. For example, compared to the other two methods, tree search is at least 10--20 times slower but leads to negligible performance gains, which hinders its real-world applications. Code and data are available at https://github.com/OSU-NLP-Group/llm-planning-eval.

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