Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
  Models
Authors: name: Ling Yang, name: Zhaochen Yu, name: Tianjun Zhang, name: Shiyi Cao, name: Minkai Xu, name: Wentao Zhang, name: Joseph E. Gonzalez, name: Bin Cui

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