Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc. However, the limited information provided by users during single turn results in inadequate personalization and targeting of the generated suggestions, which requires users to independently select the useful part. It is mainly caused by the missing ability to engage in multi-turn questioning. In real-world medical consultations, doctors usually employ a series of iterative inquiries to comprehend the patient's condition thoroughly, enabling them to provide effective and personalized suggestions subsequently, which can be defined as chain of questioning (CoQ) for LLMs. To improve the CoQ of LLMs, we propose BianQue, a ChatGLM-based LLM finetuned with the self-constructed health conversation dataset BianQueCorpus that is consist of multiple turns of questioning and health suggestions polished by ChatGPT. Experimental results demonstrate that the proposed BianQue can simultaneously balance the capabilities of both questioning and health suggestions, which will help promote the research and application of LLMs in the field of proactive health.

翻译：摘要：大语言模型在单轮对话中已能提供通用且广泛的健康建议，例如ChatGPT、ChatGLM、ChatDoctor、DoctorGLM等系统。然而，用户在单轮对话中提供的信息有限，导致生成建议的个性化和针对性不足，用户需自行筛选有用部分。这主要是因为模型缺乏多轮提问能力。在实际医疗问诊中，医生通常通过一系列迭代式提问来全面了解患者病情，从而能够随后提供有效且个性化的建议，这一过程可定义为大语言模型的"提问链"。为提升大语言模型的提问链能力，我们提出BianQue——一种基于ChatGLM微调的大语言模型，使用自建的健康对话数据集BianQueCorpus进行训练，该数据集包含多轮提问及经ChatGPT精修的健康建议。实验结果表明，BianQue能够同时平衡提问与健康建议两种能力，这将有助于推动大语言模型在主动健康领域的研究与应用。