Agent-Based ChatGPT for Multi-Party Conversation Task Processing

随着大型语言模型(LLMs)规模的显著扩展，它们在众多自然语言处理(NLP)任务中展现出了卓越的零样本学习能力，能够在无需针对特定数据集进行预训练的情况下执行任务。这些模型在多个语言相关领域，包括搜索引擎，显示了显著的泛化能力。然而，LLMs在处理多方对话(MPC)——一个涉及多个参与者进行复杂信息交流的场景——的能力尚未得到充分探索。本文旨在评估生成型LLMs，如ChatGPT和GPT-4，在MPC领域的应用潜力。我们通过在两个包含四个代表性任务的MPC数据集上对ChatGPT和GPT-4进行实证分析，具体评估了它们的零样本学习能力。研究结果表明，ChatGPT在多个MPC任务上的表现仍有提升空间，而GPT-4的结果则显示出积极的发展前景。此外，我们尝试通过整合MPC结构和代理机制，涵盖说话者架构以及与四个任务相关的代理方法，以增强模型性能。本研究全面评估了生成型LLMs在多方对话中的应用，并深入分析了构建更高效、更强大的MPC代理的理念与策略，为该领域的进步提供了新的洞见。最后，我们指出了LLMs在MPC应用中面临的挑战，特别是在解析复杂信息流和生成风格一致的响应方面，这些挑战可能会影响模型的实际应用效果。With the significant expansion of Large Language Models (LLMs), they have demonstrated remarkable zero-shot learning capabilities across various NLP tasks, performing well without specific dataset pre-training. These models have shown strong generalization in language-related fields like search engines. However, their ability in Multi-Party Conversation (MPC) scenarios, where multiple participants exchange complex information, remains underexplored. This paper evaluates the application potential of generative LLMs, such as ChatGPT and GPT-4, in the MPC domain. Through empirical analysis on two MPC datasets with four representative tasks, we assess their zero-shot learning abilities. Results show that ChatGPT has room for improvement in various MPC tasks, while GPT-4 shows promising prospects. Additionally, we enhance model performance by integrating MPC structures and agent mechanisms, covering speaker architectures and agent methods related to the four tasks. This study comprehensively evaluates generative LLMs in MPC and analyzes strategies for building more efficient and powerful MPC agents, offering new insights for the field. Finally, we highlight challenges in LLMs’ MPC applications, especially in parsing complex information flows and generating consistent responses, which may affect practical application.