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English(EN) Who Convinced Whom? Tracing How LLMs Change Their Minds in Multi-Agent Systems

辩论系统中的大型语言模型显示出更高的共识,但也存在共同的错误

一项新的研究论文介绍了一个名为MAD Bench的基准,该基准旨在评估大型语言模型(LLMs)在多智能体辩论系统中如何相互影响其决策。研究发现,大型语言模型极易受到同伴交流的影响,与独立思考相比,辩论能带来更强的共识,但也导致了更高的共同错误率。研究还强调,信息的感知来源,而非其内容,可能不成比例地影响大型语言模型的决策过程。 AI

影响 强调了大型语言模型的沟通动态如何导致准确性提高和一致性错误增加,从而影响多智能体AI系统的可靠性。

排序理由 介绍大型语言模型在多智能体系统中行为新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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辩论系统中的大型语言模型显示出更高的共识,但也存在共同的错误

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介绍大型语言模型在多智能体系统中行为新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Arnesh Batra, Aniket Khandelwal, Jashn Khemani, Krish Thukral, Arush Gumber, Denis Federico Lim, Ujwal Kumar, Phan Xuan Tan ·

    谁说服了谁?追踪大型语言模型在多智能体系统中如何改变主意

    arXiv:2610.35930v1 Announce Type: cross Abstract: Multi-agent debate (MAD) aims to improve language-model decisions through the exchange of arguments, yet agreement after debate need not reflect independent corroboration. We investigate how peer communication influences individua…