We develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business negotiations. Specifically, we simulate business negotiations by letting two LLM-based agents engage in role play. A third LLM acts as a remediator agent to rewrite utterances violating norms for improving negotiation outcomes. We introduce a simple tuning-free and label-free In-Context Learning (ICL) method to identify high-quality ICL exemplars for the remediator, where we propose a novel select criteria, called value impact, to measure the quality of the negotiation outcomes. We provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different negotiation topics. The source code and the generated dataset will be publicly available upon acceptance.

翻译：我们开发了基于大型语言模型（LLMs）的辅助代理，旨在帮助对话者进行商务谈判。具体而言，我们通过让两个基于LLM的代理进行角色扮演来模拟商务谈判。第三个LLM充当调解代理，负责重写违反规范的发言以改善谈判结果。我们引入了一种简单的免调优、免标签的上下文学习（ICL）方法，为调解代理识别高质量的ICL示例，并提出了一种称为“价值影响”的新颖选择标准来衡量谈判结果的质量。我们提供了丰富的实证证据，证明了该方法在三个不同谈判主题中的有效性。源代码和生成的数据集将在论文被接受后公开提供。