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Buffer of Thoughts

Buffer of Thoughts is a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).

Data and Resources

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Cite this as

Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E. Gonzalez, Bin Cui (2024). Dataset: Buffer of Thoughts. https://doi.org/10.57702/6z041tgi

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It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2406.04271
Author	Ling Yang
More Authors	Zhaochen Yu Tianjun Zhang Shiyi Cao Minkai Xu Wentao Zhang Joseph E. Gonzalez Bin Cui
Homepage	https://github.com/YangLing0818/buffer-of-thought-llm