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English(EN) Towards using Large Language Models for Risk Negotiation and Consensus-building in One Health contexts

大型语言模型协助人类谈判者就复杂风险达成共识

研究人员开发了一个新颖的框架,该框架将基于大型语言模型(LLM)的代理整合到人类监督的谈判过程中,以在人类健康(One Health)背景下进行风险评估和建立共识。该方法使用检索增强生成(retrieval-augmented generation)从利益相关者材料中创建代理描述,并利用这些代理访问特定领域的计算模型和网络来源。通过模拟谈判游戏,并由领域专家对涉及食品安全和病虫害管理的情景进行评估,结果表明参与者在修改提议的交易后,可以在规定时间内达成共识。 AI

影响 该框架有望提高跨领域复杂、多方利益相关者风险谈判的效率和决策质量。

排序理由 学术论文,详细介绍了在风险谈判中使用大型语言模型的创新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

大型语言模型协助人类谈判者就复杂风险达成共识

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学术论文,详细介绍了在风险谈判中使用大型语言模型的创新方法。[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) · Alexandra Fetsch, Iurii Savvateev, Racem Ben Romdhane, Martin Wiedmann, Artemiy Dimov, Maciej Durkalec, Josef Teichmann, Jakob Zinsstag, Konstantinos Koutsoumanis, Andreja Rajkovic, Jason Mann, Mauro Tonolla, Monika Ehling-Schulz, Matthias Filter, Sophia… ·

    探索在One Health背景下利用大型语言模型进行风险谈判和共识建设

    arXiv:2509.09906v2 Announce Type: replace-cross Abstract: Risk negotiation under the One Health framework requires balancing competing stakeholder interests across human, animal, and environmental health domains - a process constrained by information complexity and time pressure.…