Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
  Models
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
6662730aeaa6f8030a486dea
Ling Yang
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2024-07-12T07:41:54.895Z
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Ling Yang
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Zhaochen Yu
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Tianjun Zhang
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2024-06-07T10:20:32.839Z
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Tianjun Zhang
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Shiyi Cao
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2024-06-07T10:20:48.851Z
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Shiyi Cao
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Minkai Xu
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2024-06-07T10:20:56.488Z
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Minkai Xu
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Wentao Zhang
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Joseph E. Gonzalez
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2024-06-07T10:21:26.883Z
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Joseph E. Gonzalez
user
ProfJoeyG
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Bin Cui
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
  Models
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
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.
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