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English(EN) Narrowing the Collaboration Gap, Probably

LLM代理应沟通校准后的概率以实现更好的协作

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新的研究论文提出，在任务上协作的大型语言模型（LLM）应沟通关于行动的校准信念，而不是仅仅沟通建议的行动。这种概率沟通方法被证明在聚合与决策相关的信息方面更有效，尤其是在概率无偏见的情况下。该研究使用Transformer和LLM代理在一个迷宫解决任务中测试了这一原则，证明了在保留校准不确定性时，概率沟通优于行动沟通。 AI

影响 这项研究可以通过实现更有效的信息共享和决策来改进多智能体LLM系统。

排序理由 该集群包含一篇发表在arXiv上的研究论文，详细介绍了一种新的LLM协作方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LLM代理应沟通校准后的概率以实现更好的协作

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该集群包含一篇发表在arXiv上的研究论文，详细介绍了一种新的LLM协作方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mirah Shi, Marcel Hussing, Ira Globus-Harris, Natalie Collina, Aaron Roth, Surbhi Goel ·

    缩小协作差距，或许吧

    arXiv:2610.19215v1 Announce Type: new Abstract: Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a way that promotes aggregation of decision-relevant inf…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Surbhi Goel ·

    缩小合作差距，或许吧

    Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a way that promotes aggregation of decision-relevant information? Communicating only proposed actions ca…

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