DiagGPT：一种面向灵活任务型对话的、基于大语言模型且具备自动主题管理的多智能体对话系统
DiagGPT: An LLM-based and Multi-agent Dialogue System with Automatic Topic Management for Flexible Task-Oriented Dialogue
AI总结：
针对现有大语言模型在复杂诊断类任务型对话中表现不足的问题，本文提出具备自动主题管理功能的多智能体系统DiagGPT，可拓展大模型的任务型对话适用场景、提升交互灵活性，实验验证其性能优异。
摘要：
大语言模型（LLM，如ChatGPT）的一项重要应用是作为聊天智能体部署，可回应多个领域的人类咨询。尽管当前的LLM擅长回答通用问题，但在法律、医疗或其他专业咨询这类复杂诊断场景中往往表现不足。这类场景通常需要任务型对话（Task-Oriented Dialogue, TOD），AI聊天智能体必须主动提问，引导用户达成特定目标或完成任务。以往的微调模型在TOD任务中表现不佳，且当前LLM的对话能力潜力尚未得到充分挖掘。本文提出DiagGPT（Dialogue in Diagnosis GPT），这是一种将LLM拓展至更多TOD场景的创新方法。除了引导用户完成任务，DiagGPT还能在对话推进过程中有效管理所有主题的状态。该特性提升了用户体验，为TOD提供了更灵活的交互方式。我们的实验表明，DiagGPT在与用户开展TOD方面表现出色，展现出在各领域实际应用的潜力。
英文摘要：
A significant application of Large Language Models (LLMs), like ChatGPT, is their deployment as chat agents, which respond to human inquiries across a variety of domains. While current LLMs proficiently answer general questions, they often fall short in complex diagnostic scenarios such as legal, medical, or other specialized consultations. These scenarios typically require Task-Oriented Dialogue (TOD), where an AI chat agent must proactively pose questions and guide users toward specific goals or task completion. Previous fine-tuning models have underperformed in TOD and the full potential of conversational capability in current LLMs has not yet been fully explored. In this paper, we introduce DiagGPT (Dialogue in Diagnosis GPT), an innovative approach that extends LLMs to more TOD scenarios. In addition to guiding users to complete tasks, DiagGPT can effectively manage the status of all topics throughout the dialogue development. This feature enhances user experience and offers a more flexible interaction in TOD. Our experiments demonstrate that DiagGPT exhibits outstanding performance in conducting TOD with users, showing its potential for practical applications in various fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
