When is Tree Search Useful for LLM Planning? It Depends on the
  Discriminator
Ziru Chen Michael White Raymond Mooney Ali Payani Yu Su Huan Sun
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 will be released at
https://github.com/OSU-NLP-Group/llm-planning-eval.