Large Language Models (LLMs), such as ChatGPT, are increasingly sophisticated and exhibit capabilities closely resembling those of humans. A significant application of these LLMs is their use as chat agents, responding to human inquiries across various 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 this 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.

翻译：摘要：大语言模型（如ChatGPT）日益成熟，展现出与人类高度相似的能力。此类模型的重要应用之一，是作为对话代理，回应各领域的人类询问。尽管当前大语言模型能够熟练回答一般性问题，但在法律、医疗或其他专业咨询等复杂诊断场景中往往表现不足。这些场景通常需要任务导向对话（TOD），即AI对话代理必须主动提出问题，并引导用户达成特定目标或完成任务。以往的微调模型在TOD任务中表现欠佳，而当前大语言模型在此类能力上的全部潜力尚未得到充分挖掘。本文提出DiagGPT（诊断式对话GPT），这是一种将大语言模型扩展到更多TOD场景的创新方法。除了引导用户完成任务外，DiagGPT还能在对话发展过程中有效管理所有主题的状态。这一特性增强了用户体验，并在TOD中提供更灵活的交互方式。实验表明，DiagGPT在与用户进行任务导向对话时展现出卓越性能，彰显其在各领域实际应用中的潜力。