@inproceedings{
yang2024buffer,
title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},
author={Ling Yang and Zhaochen Yu and Tianjun Zhang and Shiyi Cao and Minkai Xu and Wentao Zhang and Joseph E. Gonzalez and Bin CUI},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ANO1i9JPtb}
}
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. Code is available at: https://github.com/YangLing0818/buffer-of-thought-llm
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Ling Yang Zhaochen Yu Tianjun Zhang Shiyi Cao Minkai Xu Wentao Zhang Joseph E. Gonzalez Bin CUI