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.

翻译：随着大型语言模型（LLM）日益受到重视，基于人工智能的辅导系统开发也日益增多。这些系统对于为代表性不足的群体提供更好的优质教育机会至关重要。谈判相关的战略性议价是许多学习者无法接触到的一个重要教育领域。为此，我们开发了基于LLM的谈判辅导助手（ACE）。ACE不仅可以作为用户的谈判对手，还能为用户提供有针对性的改进反馈。为构建该系统，我们收集了MBA学生之间的谈判对话记录数据集。这些记录来自受过训练的谈判者，并模拟了真实的议价场景。我们利用该数据集并结合专家咨询，设计了一套用于检测谈判失误的标注方案。ACE运用此方案来识别失误并向用户提供针对性反馈。为测试ACE生成反馈的有效性，我们进行了一项用户实验，包含连续两轮谈判试验。结果表明，与不提供反馈的系统以及采用其他反馈方法的系统相比，ACE能显著提升用户的谈判表现。