title: Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
    Models
summary: We introduce Buffer of Thoughts (BoT), a novel and versatile
  thought-augmented reasoning approach for enhancing accuracy, efficiency and
  robustness of large language models (LLMs). Specifically, we propose
  meta-buffer to store a series of informative high-level thoughts, namely
  thought-template, distilled from the problem-solving processes across various
  tasks. Then for each problem, we retrieve a relevant thought-template and
  adaptively instantiate it with specific reasoning structures to conduct
  efficient reasoning. To guarantee the scalability and stability, we further
  propose buffer-manager to dynamically update the meta-buffer, thus enhancing
  the capacity of meta-buffer as more tasks are solved. We conduct extensive
  experiments on 10 challenging reasoning-intensive tasks, and achieve
  significant performance improvements over previous SOTA methods: 11% on Game of
  24, 20% on Geometric Shapes and 51% on Checkmate-in-One. Further analysis
  demonstrate the superior generalization ability and model robustness of our
  BoT, while requiring only 12% of the cost of multi-query prompting methods
  (e.g., tree/graph of thoughts) on average. Notably, we find that our
  Llama3-8B+BoT has the potential to surpass Llama3-70B model. Our project is
  available at: https://github.com/YangLing0818/buffer-of-thought-llm
paper_authors._id: 6662730aeaa6f8030a486dea
paper_authors.name: Ling Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Ling Yang
paper_authors.type: user
paper_authors.user: Lingaaaaaaa
paper_authors._id: 6662730aeaa6f8030a486deb
paper_authors.name: Zhaochen Yu
paper_authors._id: 6662730aeaa6f8030a486dec
paper_authors.name: Tianjun Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Tianjun Zhang
paper_authors.type: user
paper_authors.user: tianjunz
paper_authors._id: 6662730aeaa6f8030a486ded
paper_authors.name: Shiyi Cao
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paper_authors.fullname: Shiyi Cao
paper_authors.type: user
paper_authors.user: eva98
paper_authors._id: 6662730aeaa6f8030a486dee
paper_authors.name: Minkai Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-07T10:20:56.488Z
paper_authors._id: 64c0e950aa57599de1c75dad
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paper_authors.fullname: Minkai Xu
paper_authors.type: user
paper_authors.user: mkxu
paper_authors._id: 6662730aeaa6f8030a486def
paper_authors.name: Wentao Zhang
paper_authors._id: 6662730aeaa6f8030a486df0
paper_authors.name: Joseph E. Gonzalez
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-07T10:21:26.883Z
paper_authors._id: 645d2e8401f4eaab2a0878ce
paper_authors.avatarUrl: /avatars/1273c5fb607b4b622a746a42692fa632.svg
paper_authors.fullname: Joseph E. Gonzalez
paper_authors.type: user
paper_authors.user: ProfJoeyG
paper_authors._id: 6662730aeaa6f8030a486df1
paper_authors.name: Bin Cui
paper_title: Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
    Models
paper_summary: We introduce Buffer of Thoughts (BoT), a novel and versatile
  thought-augmented reasoning approach for enhancing accuracy, efficiency and
  robustness of large language models (LLMs). Specifically, we propose
  meta-buffer to store a series of informative high-level thoughts, namely
  thought-template, distilled from the problem-solving processes across various
  tasks. Then for each problem, we retrieve a relevant thought-template and
  adaptively instantiate it with specific reasoning structures to conduct
  efficient reasoning. To guarantee the scalability and stability, we further
  propose buffer-manager to dynamically update the meta-buffer, thus enhancing
  the capacity of meta-buffer as more tasks are solved. We conduct extensive
  experiments on 10 challenging reasoning-intensive tasks, and achieve
  significant performance improvements over previous SOTA methods: 11% on Game of
  24, 20% on Geometric Shapes and 51% on Checkmate-in-One. Further analysis
  demonstrate the superior generalization ability and model robustness of our
  BoT, while requiring only 12% of the cost of multi-query prompting methods
  (e.g., tree/graph of thoughts) on average. Notably, we find that our
  Llama3-8B+BoT has the potential to surpass Llama3-70B model. Our project is
  available at: https://github.com/YangLing0818/buffer-of-thought-llm
paper_ai_summary: Buffer of Thoughts (BoT) enhances large language models with a meta-buffer of adaptive thought-templates, improving performance, efficiency, and robustness across various reasoning tasks.
paper_ai_keywords: thought-augmented reasoning large language models meta-buffer thought-template buffer-manager Game of 24 Geometric Shapes Checkmate-in-One generalization ability robustness multi-query prompting methods