Recent researches of large language models(LLM), which is pre-trained on massive general-purpose corpora, have achieved breakthroughs in responding human queries. However, these methods face challenges including limited data insufficiency to support extensive pre-training and can not align responses with users' instructions. To address these issues, we introduce a medical instruction dataset, CMedINS, containing six medical instructions derived from actual medical tasks, which effectively fine-tunes LLM in conjunction with other data. Subsequently, We launch our medical model, IIMedGPT, employing an efficient preference alignment method, Direct preference Optimization(DPO). The results show that our final model outperforms existing medical models in medical dialogue.Datsets, Code and model checkpoints will be released upon acceptance.

本研究解决了大型语言模型在医学任务中面临的数据不足和对用户指令对齐能力不足的问题。通过引入实际医学任务的CMedINS数据集，并采用直接偏好优化方法（DPO），本文提出的IIMedGPT模型在医学对话任务中显著优于现有模型，具备更好的实用性和精准性。

IIMedGPT：通过高效的人类偏好对齐促进医学任务的语言模型能力