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Computer Science > Computation and Language

arXiv:2308.09687 (cs)
[Submitted on 18 Aug 2023 (v1), last revised 21 Aug 2023 (this
version, v2)]

Title:Graph of Thoughts: Solving Elaborate Problems with Large
Language Models

Authors:Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger,
Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski,
Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler
Download a PDF of the paper titled Graph of Thoughts: Solving
Elaborate Problems with Large Language Models, by Maciej Besta and 10
other authors
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    Abstract: We introduce Graph of Thoughts (GoT): a framework that
    advances prompting capabilities in large language models (LLMs)
    beyond those offered by paradigms such as Chain-of-Thought or
    Tree of Thoughts (ToT). The key idea and primary advantage of GoT
    is the ability to model the information generated by an LLM as an
    arbitrary graph, where units of information ("LLM thoughts") are
    vertices, and edges correspond to dependencies between these
    vertices. This approach enables combining arbitrary LLM thoughts
    into synergistic outcomes, distilling the essence of whole
    networks of thoughts, or enhancing thoughts using feedback loops.
    We illustrate that GoT offers advantages over state of the art on
    different tasks, for example increasing the quality of sorting by
    62% over ToT, while simultaneously reducing costs by >31%. We
    ensure that GoT is extensible with new thought transformations
    and thus can be used to spearhead new prompting schemes. This
    work brings the LLM reasoning closer to human thinking or brain
    mechanisms such as recurrence, both of which form complex
    networks.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2308.09687 [cs.CL]
          (or arXiv:2308.09687v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2308.09687
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Maciej Besta [view email]
[v1] Fri, 18 Aug 2023 17:29:23 UTC (839 KB)
[v2] Mon, 21 Aug 2023 10:51:42 UTC (839 KB)
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