The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the success of large language models (LLMs) in complex reasoning tasks in various fields, such as mathematics, logic, and commonsense QA, they still need to improve with the increased complexity and specialization of the medical field. This is because medical CQA tasks require not only strong medical reasoning, but also the ability to think broadly and deeply. In this paper, to address these challenges in medical CQA tasks that need to be considered and understood in many aspects, we propose the Holistically Thought (HoT) method, which is designed to guide the LLMs to perform the diffused and focused thinking for generating high-quality medical responses. The proposed HoT method has been evaluated through automated and manual assessments in three different medical CQA datasets containing the English and Chinese languages. The extensive experimental results show that our method can produce more correctness, professional, and considerate answers than several state-of-the-art (SOTA) methods, manifesting its effectiveness. Our code in https://github.com/WENGSYX/HoT.

翻译：医疗对话问答系统旨在提供一系列专业医疗服务，以提升医疗效率。尽管大语言模型在数学、逻辑和常识问答等领域的复杂推理任务中取得了成功，但随着医疗领域复杂性和专业性的增加，它们仍需改进。这是因为医疗对话问答任务不仅需要强大的医学推理能力，还需具备广泛而深入的思考能力。本文针对医疗对话问答任务中需要在多方面进行考虑和理解的挑战，提出了整体性思考方法，旨在引导大语言模型进行扩散性和聚焦性思考，从而生成高质量的医疗回复。所提出的整体性思考方法在三个包含英语和中文的不同医疗对话问答数据集上，通过自动评估和人工评估进行了验证。大量实验结果表明，与多种最先进方法相比，我们的方法能生成更正确、更专业且更周全的答案，证明了其有效性。代码见：https://github.com/WENGSYX/HoT。