Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted. In this paper, we delve into the potential of generative LLMs such as ChatGPT and GPT-4 within the context of MPCs. An empirical analysis is conducted to assess the zero-shot learning capabilities of ChatGPT and GPT-4 by subjecting them to evaluation across three MPC datasets that encompass five representative tasks. The findings reveal that ChatGPT's performance on a number of evaluated MPC tasks leaves much to be desired, whilst GPT-4's results portend a promising future. Additionally, we endeavor to bolster performance through the incorporation of MPC structures, encompassing both speaker and addressee architecture. This study provides an exhaustive evaluation and analysis of applying generative LLMs to MPCs, casting a light upon the conception and creation of increasingly effective and robust MPC agents. Concurrently, this work underscores the challenges implicit in the utilization of LLMs for MPCs, such as deciphering graphical information flows and generating stylistically consistent responses.

翻译：大型语言模型（LLMs）已成为自然语言处理领域的重要工具，但其处理多方对话（MPCs）——即涉及多个对话者参与复杂信息交换的场景——的能力尚不明确。本文探究了ChatGPT、GPT-4等生成式LLMs在MPC场景中的潜力。通过在三组涵盖五项代表性任务的MPC数据集上评估ChatGPT和GPT-4的零样本学习能力，实证分析表明：ChatGPT在多项MPC任务中的表现仍有较大提升空间，而GPT-4的结果则预示了光明前景。此外，我们尝试通过融入包含说话者和听话者架构的MPC结构来提升性能。本研究对生成式LLMs在MPC中的应用进行了全面评估与分析，为设计更高效、更鲁棒的MPC智能体提供了启示，同时揭示了将LLMs用于MPC所面临的挑战，如解析图形信息流与生成风格一致的回复。