本文提出 LoT 框架，利用符号逻辑中的反证法逐步验证和纠正推理过程，显著提升了大型语言模型在多领域的零样本思维链推理能力。
摘要：
大型语言模型的最新进展展示了其在各个领域卓越的泛化能力。然而，它们的推理能力仍有很大的提升空间，尤其是在面对需要多步推理的场景时。尽管大型语言模型拥有广泛的知识，但其推理往往未能有效利用这些知识来建立连贯的思维范式。由于推理过程不受逻辑原则的约束，这些模型有时会出现幻觉。旨在提升大型语言模型的零样本思维链推理能力，我们提出了 LoT (Logical Thoughts)，这是一种自我改进的提示框架，利用植根于符号逻辑的原则，特别是反证法，逐步系统地验证和纠正推理过程。在涵盖算术、常识、符号、因果推断和社会问题等不同领域的语言任务上进行的实验评估证明了通过逻辑增强推理的有效性。LoT 的实现代码可在 https://github.com/xf-zhao/LoT 获取。
英文摘要：
Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning ability of large language models, we propose LoT (Logical Thoughts), a self-improvement prompting framework that leverages principles rooted in symbolic logic, particularly Reductio ad Absurdum, to systematically verify and rectify the reasoning processes step by step. Experimental evaluations conducted on language tasks in diverse domains, including arithmetic, commonsense, symbolic, causal inference, and social problems, demonstrate the efficacy of enhanced reasoning by logic. The implementation code for LoT can be accessed at: https://github.com/xf-zhao/LoT.
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School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology（北京理工大学海量语言信息处理与云计算应用北京工程研究中心）
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RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Portland State University（波特兰州立大学）；Renmin University of China（中国人民大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Dalian Maritime University（大连海事大学）；University of Kansas（堪萨斯大学）
FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦女王学院电子工程与计算机科学学院）；Department of Knowledge Technologies, Jožef Stefan Institute（Jožef Stefan研究所知识技术系）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
