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arXiv 2410.04463cs.CL
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Wrong-of-Thought:一种融合多视角验证与错误信息的推理框架

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

School of Computer Science and Engineering, Central South University(中南大学计算机科学与工程学院);Research Center for SCIR, Harbin Institute of Technology(哈尔滨工业大学SCIR研究中心);Qilu University of Technology (Shandong Academy of Sciences)(齐鲁工业大学(山东省科学院));Yunnan University(云南大学)

Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

AI总结:

针对大语言模型思维链推理中验证方法单一、错误信息被忽视的问题,提出包含多视角验证与错误信息利用模块的Wrong-of-Thought框架,在多数据集多模型上性能超越基线,难计算任务表现优异。

摘要:

思维链(Chain-of-Thought, CoT)已成为提升大语言模型(Large Language Models, LLMs)性能的关键技术,受到研究者越来越多的关注。一类方法通过持续验证并优化LLMs的推理输出以达到预期质量,实现LLMs的迭代增强。尽管这类方法效果显著,但该范式存在两个关键问题:(1)验证方法单一:当前范式仅依赖单一的验证方法;(2)错误信息被忽视:传统范式在推理过程中直接忽略错误信息,每次都要从头重构逻辑路径。为应对这些挑战,我们提出Wrong-of-Thought(WoT)框架,包含两个核心模块:(1)多视角验证:一种多视角验证方法,用于精准优化推理过程与结果;(2)错误信息利用:利用错误信息对LLMs进行警示,降低其重复犯错的概率。在8个主流数据集和5个LLMs上的实验表明,WoT优于所有现有基线方法。此外,WoT在高难度计算任务中展现出强大的能力。

英文摘要:

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Simple verification methods: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: Traditional paradigms directly ignore wrong information during reasoning and refine the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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