Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement

@inproceedings{xi-etal-2023-self,
    title = "Self-{P}olish: Enhance Reasoning in Large Language Models via Problem Refinement",
    author = "Xi, Zhiheng  and
      Jin, Senjie  and
      Zhou, Yuhao  and
      Zheng, Rui  and
      Gao, Songyang  and
      Liu, Jia  and
      Gui, Tao  and
      Zhang, Qi  and
      Huang, Xuanjing",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.762",
    doi = "10.18653/v1/2023.findings-emnlp.762",
    pages = "11383--11406",
    abstract = "To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales. However, they have inadvertently overlooked the potential of enhancing model reasoning performance by formulating higher-quality problems. In this work, we start from the problem side and propose Self-Polish (SP), a novel method that facilitates the model{'}s reasoning by guiding it to progressively refine the given problems to be more comprehensible and solvable. We also explore several automatic prompting varients and propose the Self-Polish prompt bank for the community. SP is orthogonal to all other prompting methods of answer/reasoning side like CoT, allowing for seamless integration with state-of-the-art techniques for further improvement. Thorough experiments show that the proposed method attains notable and consistent effectiveness on five reasoning benchmarks across different models. Furthermore, our method also showcases impressive performance on robustness evaluation. Codes and prompts are available at https://github.com/WooooDyy/Self-Polish.",
}

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    <abstract>To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales. However, they have inadvertently overlooked the potential of enhancing model reasoning performance by formulating higher-quality problems. In this work, we start from the problem side and propose Self-Polish (SP), a novel method that facilitates the model’s reasoning by guiding it to progressively refine the given problems to be more comprehensible and solvable. We also explore several automatic prompting varients and propose the Self-Polish prompt bank for the community. SP is orthogonal to all other prompting methods of answer/reasoning side like CoT, allowing for seamless integration with state-of-the-art techniques for further improvement. Thorough experiments show that the proposed method attains notable and consistent effectiveness on five reasoning benchmarks across different models. Furthermore, our method also showcases impressive performance on robustness evaluation. Codes and prompts are available at https://github.com/WooooDyy/Self-Polish.</abstract>
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%0 Conference Proceedings
%T Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
%A Xi, Zhiheng
%A Jin, Senjie
%A Zhou, Yuhao
%A Zheng, Rui
%A Gao, Songyang
%A Liu, Jia
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F xi-etal-2023-self
%X To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales. However, they have inadvertently overlooked the potential of enhancing model reasoning performance by formulating higher-quality problems. In this work, we start from the problem side and propose Self-Polish (SP), a novel method that facilitates the model’s reasoning by guiding it to progressively refine the given problems to be more comprehensible and solvable. We also explore several automatic prompting varients and propose the Self-Polish prompt bank for the community. SP is orthogonal to all other prompting methods of answer/reasoning side like CoT, allowing for seamless integration with state-of-the-art techniques for further improvement. Thorough experiments show that the proposed method attains notable and consistent effectiveness on five reasoning benchmarks across different models. Furthermore, our method also showcases impressive performance on robustness evaluation. Codes and prompts are available at https://github.com/WooooDyy/Self-Polish.
%R 10.18653/v1/2023.findings-emnlp.762
%U https://aclanthology.org/2023.findings-emnlp.762
%U https://doi.org/10.18653/v1/2023.findings-emnlp.762
%P 11383-11406

Markdown (Informal)

[Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement](https://aclanthology.org/2023.findings-emnlp.762) (Xi et al., Findings 2023)

ACL

Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023. Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11383–11406, Singapore. Association for Computational Linguistics.

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