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English(EN) Distilling Bayesian Belief States into Language Models for Auditable Negotiation

新的BOND框架使用提炼后的LLM实现可审计的谈判

研究人员开发了一个名为BOND(贝叶斯对手信念谈判提炼)的新框架,以使谈判代理更具可审计性。该系统使用大型语言模型在对话中推断和更新关于对手价值的信念。然后,一个较小的、提炼后的模型利用这些信念来做出决策,并可以输出归一化的后验信念,在CaSiNo谈判数据集上表现优于现有的最先进方法。 AI

影响 引入了一种使基于LLM的谈判代理更透明和可审计的方法。

排序理由 这是一篇详细介绍AI谈判代理新框架的研究论文。

在 arXiv cs.CL 阅读 →

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新的BOND框架使用提炼后的LLM实现可审计的谈判

报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Zongqi Cui, Baihan Lin ·

    Distilling Bayesian Belief States into Language Models for Auditable Negotiation

    arXiv:2605.04507v1 Announce Type: new Abstract: Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end large language models (LLMs) can imitate negotiation dialogue, but their opponen…

  2. arXiv cs.CL TIER_1 English(EN) · Baihan Lin ·

    Distilling Bayesian Belief States into Language Models for Auditable Negotiation

    Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end large language models (LLMs) can imitate negotiation dialogue, but their opponent beliefs are usually implicit and difficult to …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Distilling Bayesian Belief States into Language Models for Auditable Negotiation

    Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end large language models (LLMs) can imitate negotiation dialogue, but their opponent beliefs are usually implicit and difficult to …