用大模型打造谈判教练，能实时指出错误并提升谈判表现。
ACE: A LLM-based Negotiation Coaching System
- 基于MBA学生谈判记录构建数据集，设计错误标注方案。
- 实验显示，使用该系统反馈后谈判表现显著优于无反馈或旧方法。
- 适合想提升谈判能力但缺乏导师指导的学习者。
大型语言模型的兴起推动了AI辅导系统的快速发展，这类系统对改善弱势群体的教育可及性至关重要。然而，战略谈判这类关键技能仍难普及。为此，我们开发了基于大模型的谈判教练系统（ACE），不仅能作为谈判对手，还能提供针对性改进反馈。我们收集了来自受训谈判者的MBA学生谈判对话数据，模拟真实谈判场景，并结合专家意见设计了谈判失误的标注方案。ACE利用该方案识别用户错误并生成反馈。通过两轮连续谈判的用户实验验证，结果显示，与无反馈系统及替代反馈方法相比，使用ACE反馈能显著提升谈判表现。
原文摘要 · Abstract (English)
The growing prominence of LLMs has led to an increase in the development of AI tutoring systems. These systems are crucial in providing underrepresented populations with improved access to valuable education. One important area of education that is unavailable to many learners is strategic bargaining related to negotiation. To address this, we develop a LLM-based Assistant for Coaching nEgotiation (ACE). ACE not only serves as a negotiation partner for users but also provides them with targeted feedback for improvement. To build our system, we collect a dataset of negotiation transcripts between MBA students. These transcripts come from trained negotiators and emulate realistic bargaining scenarios. We use the dataset, along with expert consultations, to design an annotation scheme for detecting negotiation mistakes. ACE employs this scheme to identify mistakes and provide targeted feedback to users. To test the effectiveness of ACE-generated feedback, we conducted a user experiment with two consecutive trials of negotiation and found that it improves negotiation performances significantly compared to a system that doesn't provide feedback and one which uses an alternative method of providing feedback.
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