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Computer Science > Computation and Language

arXiv:2305.10601 (cs)
[Submitted on 17 May 2023]

Title:Tree of Thoughts: Deliberate Problem Solving with Large
Language Models

Authors:Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L.
Griffiths, Yuan Cao, Karthik Narasimhan
Download a PDF of the paper titled Tree of Thoughts: Deliberate
Problem Solving with Large Language Models, by Shunyu Yao and 6 other
authors
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    Abstract: Language models are increasingly being deployed for
    general problem solving across a wide range of tasks, but are
    still confined to token-level, left-to-right decision-making
    processes during inference. This means they can fall short in
    tasks that require exploration, strategic lookahead, or where
    initial decisions play a pivotal role. To surmount these
    challenges, we introduce a new framework for language model
    inference, Tree of Thoughts (ToT), which generalizes over the
    popular Chain of Thought approach to prompting language models,
    and enables exploration over coherent units of text (thoughts)
    that serve as intermediate steps toward problem solving. ToT
    allows LMs to perform deliberate decision making by considering
    multiple different reasoning paths and self-evaluating choices to
    decide the next course of action, as well as looking ahead or
    backtracking when necessary to make global choices. Our
    experiments show that ToT significantly enhances language models'
    problem-solving abilities on three novel tasks requiring
    non-trivial planning or search: Game of 24, Creative Writing, and
    Mini Crosswords. For instance, in Game of 24, while GPT-4 with
    chain-of-thought prompting only solved 4% of tasks, our method
    achieved a success rate of 74%. Code repo with all prompts: this
    https URL.

Comments: Code repo with all prompts: this https URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2305.10601 [cs.CL]
          (or arXiv:2305.10601v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2305.10601
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Shunyu Yao [view email]
[v1] Wed, 17 May 2023 23:16:17 UTC (609 KB)
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