通过置信度表达增强多智能体辩论系统性能
Enhancing Multi-Agent Debate System Performance via Confidence Expression
AI总结：
针对多智能体辩论（MAD）系统中因缺乏或不当表达置信度导致智能体固执己见或过早收敛的问题，提出ConfMAD框架，通过在辩论全过程集成置信度表达，有效提升了系统性能并揭示了置信度对辩论动态的影响。
摘要：
生成式大型语言模型（LLMs）在广泛任务中展现出卓越性能。近期研究引入了多智能体辩论（MAD）系统，利用多个LLM模拟人类辩论从而提升任务表现。然而，尽管部分LLM在特定任务上具备更优的知识或推理能力，它们却常难以在辩论中清晰传达这一优势，部分原因在于缺乏置信度表达。此外，不当的置信度表达会导致MAD系统中的智能体要么顽固坚持错误信念，要么过早收敛于次优答案，最终削弱辩论效果与整体系统性能。为应对这些挑战，我们提出将置信度表达融入MAD系统，使LLM能够明确传达其置信水平。为验证该方法，我们开发了ConfMAD，这是一个在整个辩论过程中集成置信度表达的MAD框架。实验结果证明了该方法的有效性，我们进一步分析了置信度如何影响辩论动态，为设计置信度感知的MAD系统提供了见解。
英文摘要：
Generative Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Recent research has introduced Multi-Agent Debate (MAD) systems, which leverage multiple LLMs to simulate human debate and thereby improve task performance. However, while some LLMs may possess superior knowledge or reasoning capabilities for specific tasks, they often struggle to clearly communicate this advantage during debates, in part due to a lack of confidence expression. Moreover, inappropriate confidence expression can cause agents in MAD systems to either stubbornly maintain incorrect beliefs or converge prematurely on suboptimal answers, ultimately reducing debate effectiveness and overall system performance. To address these challenges, we propose incorporating confidence expression into MAD systems to allow LLMs to explicitly communicate their confidence levels. To validate this approach, we develop ConfMAD, a MAD framework that integrates confidence expression throughout the debate process. Experimental results demonstrate the effectiveness of our method, and we further analyze how confidence influences debate dynamics, offering insights into the design of confidence-aware MAD systems.
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。
