Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
<|reference_start|>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<|reference_end|>
@article{yang2024buffer,
    title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language
  Models},
    author={Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao
  Zhang, Joseph E. Gonzalez, Bin Cui},
    journal={arXiv preprint arXiv:2406.04271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04271},
    primaryClass={cs.CL}
}