Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct doctor downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. Our model and dataset will be open-source for community development.

翻译：开发智能儿科咨询系统为提高诊断效率提供了广阔前景，尤其是在医疗资源匮乏的中国。尽管近期针对中文医学的大型语言模型（LLMs）取得了进展，但由于指令数据不足和训练流程存在缺陷，其在儿科应用中的表现仍不理想。为解决上述问题，本文构建了PedCorpus——一个从儿科教材、诊疗指南和知识图谱资源中提取的包含30万余条多任务指令的高质量数据集，以满足多样化的诊断需求。基于精心设计的PedCorpus，我们提出了儿科GPT，这是首个通过系统化鲁棒训练流程构建的中文儿科LLM助手。在持续预训练阶段，我们引入混合指令预训练机制，以缓解LLMs在医学领域适应过程中内部注入知识的不一致性问题。随后采用全参数监督微调（SFT）将通用医学知识框架融入模型。此后，我们设计了直接跟随偏好优化方法，以增强生成类儿科医师人文关怀回复的能力。在参数高效的二次SFT阶段，提出通用-特定专家混合策略，以解决医学通才能力与儿科专业知识掌握之间的胜任力冲突。基于指标评估、GPT-4评估及医生评估在不同临床下游任务上的广泛实验结果表明，儿科GPT持续优于既往的中文医学LLMs。我们的模型与数据集将开源以供社区发展。