@inproceedings{yibin-etal-2024-self,
    title = "Self-Guide:一种基于自我规划的大语言模型推理增强方法(Self-Guide: Enhancing {LLM} Reasoning Ability via Self-Plan)",
    author = "Liu, Yibin  and
      Liu, Zhenghao  and
      Yan, Yukun  and
      Yu, Shi  and
      Wang, Shuo  and
      Yang, Liner  and
      Chen, Huimin  and
      Gu, Yu  and
      Yu, Ge",
    editor = "Maosong, Sun  and
      Jiye, Liang  and
      Xianpei, Han  and
      Zhiyuan, Liu  and
      Yulan, He",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/author-degibert/2024.ccl-1.67/",
    pages = "853--869",
    language = "zho",
    abstract = "``尽管大语言模型在自然语言处理任务中取得显著进展,但其在复杂问题推理等领域还面临着认知负荷问题,即大语言模型在推理过程需要记忆并处理大量信息。因此,如何有效地减少语言模型推理过程中的认知负荷,缓解推理过程中可能出现的认知过载是一个亟待解决的问题。对此本文提出了Self-Guide方法,用于增强语言模型的推理能力。该方法通过指引大语言模型生成常识知识和推理指导,让语言模型基于自我规划来增强其推理能力,并通过与推理链结合的方式对模型的推理过程进行校准。与现有方法不同的是,本文在不对大语言模型进行微调或使用外部工具的情况下,显著提升了语言模型的推理性能。实验结果表明,Self-Guide方法在四种常见推理任务上性能显著优于基线方法,同时相比传统的推理链模型,Self-Guide方法在推理能力较弱的模型上也具有良好的泛化性能。通过结合大语言模型的自我规划和推理能力,Self-Guide方法为提升语言模型的推理能力提供了一种新的有效途径。''"
}