用大模型理解谈判对话，提升多方协商中偏好估计的准确性
Preference Estimation via Opponent Modeling in Multi-Agent Negotiation
- 结合大模型语义理解与贝叶斯框架，将对话内容转为概率信念
- 在多方谈判基准上，偏好估计准确率和一致率显著提升
- 适合需要精准理解对方意图的智能协商系统研发者
复杂多方、多议题场景下的自动协商高度依赖对手建模的准确性。然而，传统仅依赖数值的方法无法捕捉自然语言互动中的定性信息，导致偏好估计不稳定且不完整。尽管大语言模型（LLMs）能实现对话语的丰富语义理解，但如何将此类信息定量融入统一的对手建模仍具挑战。为此，我们提出一种新方法，将自然语言信息整合进结构化的贝叶斯对手建模框架。该方法利用大模型从对话中提取定性线索，并将其转化为概率形式以实现动态信念追踪。在多方谈判基准上的实验表明，融合概率推理与自然语言理解后，全一致性率和偏好估计准确率均得到提升。
原文摘要 · Abstract (English)
Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.
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