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.

通过创建高质量数据集 PedCorpus 和应用系统性和稳健的训练流程构建了中国首个儿科大型语言模型助手 PediatricsGPT，以改善诊断效率，解决中国医疗资源匮乏的问题。