思维审判提示：一种受法庭启发的用于大型语言模型二元逻辑推理的框架
Judgment-of-Thought Prompting: A Courtroom-Inspired Framework for Binary Logical Reasoning with Large Language Models
AI总结：
本文提出法庭启发的思维审判（JoT）多智能体提示框架，通过律师、检察官和法官角色辩论评估，在二元逻辑推理任务中显著提升准确性，如布尔表达式达98%准确率。
摘要：
本文提出了一种新颖的提示方法——思维审判（Judgment of Thought, JoT），专门针对二元逻辑推理任务而设计。尽管提示工程取得了进展，但现有方法在处理复杂逻辑推理任务时仍面临局限性。为解决这些问题，JoT引入了一种多智能体方法，包含三个专门角色——律师、检察官和法官——其中高级模型充当法官，低级模型担任律师和检察官，系统性地辩论和评估论点。在BigBenchHard和Winogrande等基准上的实验评估表明，与现有提示方法相比，JoT表现出优越的性能，取得了显著改进，包括在布尔表达式中达到98%的准确率。此外，我们的消融研究验证了每个角色、迭代细化循环和反馈机制的关键贡献。因此，JoT显著提高了二元推理任务的准确性、可靠性和一致性，并显示出实际应用的潜力。
英文摘要：
This paper proposes a novel prompting approach, Judgment of Thought (JoT), specifically tailored for binary logical reasoning tasks. Despite advances in prompt engineering, existing approaches still face limitations in handling complex logical reasoning tasks. To address these issues, JoT introduces a multi-agent approach with three specialized roles$\unicode{x2010}$$\unicode{x2010}$$\unicode{x2010}$lawyer, prosecutor, and judge$\unicode{x2010}$$\unicode{x2010}$$\unicode{x2010}$where a high-level model acts as the judge, and lower-level models serve as lawyer and prosecutor to systematically debate and evaluate arguments. Experimental evaluations on benchmarks such as BigBenchHard and Winogrande demonstrate JoT's superior performance compared to existing prompting approaches, achieving notable improvements, including 98\% accuracy in Boolean expressions. Also, our ablation studies validate the critical contribution of each role, iterative refinement loops, and feedback mechanisms. Consequently, JoT significantly enhances accuracy, reliability, and consistency in binary reasoning tasks and shows potential for practical applications.
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。
