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English(EN) Candidate supply and answer selection shape the value of LLM judging in multi-agent systems

多智能体系统中的LLM评判在评估准确性方面表现不一

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探讨了使用大型语言模型（LLM）进行评估的多智能体系统（MAS）的有效性。第一篇论文侧重于客观问答，发现虽然生成的候选答案中通常包含正确答案，但系统仍可能收敛于错误答案。研究还表明，评判者的可靠性因任务而异，并且将答案频率与评判者评估相结合可将准确性从63.82%提高到70%以上。第二篇论文研究了主观评估，发现单一评判者基线通常优于多智能体共识，特别是在严格的角色扮演引入了共识无法纠正的向下偏差时。这种偏差可能导致人为的一致，牺牲了与人类的一致性。 AI

影响 这些研究突显了在使用基于LLM的多智能体系统进行评估时可能存在的陷阱，表明需要仔细设计以确保准确性和与人类的一致性。

排序理由 两篇在arXiv上发表的学术论文，讨论了多智能体系统中LLM的评估方法。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

多智能体系统中的LLM评判在评估准确性方面表现不一

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两篇在arXiv上发表的学术论文，讨论了多智能体系统中LLM的评估方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Jia-Hao Ji, Sijie Li, Jiabei Cheng, Zixi She, Jin-Tai Yu, Zhiyuan Yuan ·

    候选者供给与答案选择塑造了多智能体系统中LLM评判的价值

    arXiv:2608.25937v2 Announce Type: replace Abstract: Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is difficult because generation, communication and final answer-selection rules usually…

  2. arXiv cs.CL TIER_1 English(EN) · Minsoo Song, Chanwoo Kim, Sugyeong Eo, Chanjun Park ·

    超越共识：多智能体LLM裁判在主观评估中的向下偏差和角色不对称性

    arXiv:2608.30373v1 Announce Type: new Abstract: Multi-Agent Debate (MAD) has been widely adopted to improve LLM-based evaluation by prompting multiple agents to negotiate and reach a consensus. However, for subjective rubric-based scoring, inter-agent agreement does not guarantee…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Zhiyuan Yuan ·

    候选者供给和答案选择塑造了多智能体系统中LLM评判的价值

    Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is difficult because generation, communication and final answer-selection rules usually change simultaneously. We conceptualize multi-agent…

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