@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 = "Yibin, Liu and
Zhenghao, Liu and
Yukun, Yan and
Shi, Yu and
Shuo, Wang and
Liner, Yang and
Huimin, Chen and
Yu, Gu and
Ge, Yu",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
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://aclanthology.org/2024.ccl-1.67/",
pages = "853--869",
language = "zho",
abstract = "{\textquotedblleft}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。{\textquotedblright}"
}
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<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。”</abstract>
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%0 Conference Proceedings
%T 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)
%A Yibin, Liu
%A Zhenghao, Liu
%A Yukun, Yan
%A Shi, Yu
%A Shuo, Wang
%A Liner, Yang
%A Huimin, Chen
%A Yu, Gu
%A Ge, Yu
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F yibin-etal-2024-self
%X “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。”
%U https://aclanthology.org/2024.ccl-1.67/
%P 853-869
Markdown (Informal)
[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)](https://aclanthology.org/2024.ccl-1.67/) (Yibin et al., CCL 2024)
ACL
- Liu Yibin, Liu Zhenghao, Yan Yukun, Yu Shi, Wang Shuo, Yang Liner, Chen Huimin, Gu Yu, and Yu Ge. 2024. 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). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 853–869, Taiyuan, China. Chinese Information Processing Society of China.