A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.

翻译：成功的谈判要求深度理解对话语境、运用心智理论（ToM）能力推断对方动机，以及策略推理和有效沟通，这对自动化系统构成了挑战。鉴于大型语言模型（LLMs）在各类自然语言处理任务中的卓越表现，本研究旨在探究LLMs如何推动谈判研究的不同方面，包括设计对话系统、提供教学反馈以及扩大数据收集实践。为此，我们提出了一种方法论，用于分析LLMs在覆盖典型谈判交互全过程的时间阶段的多样化对话场景中的多维度能力。我们的分析进一步增加了对GPT-4在各类任务中优越性的证据，同时也揭示了LLMs仍难以应对的特定任务。例如，在主观评估谈判对话时，模型与人类玩家的相关性较低，且常常难以生成既符合语境又具有战略优势的回应。