Integrating Physician Diagnostic Logic into Large Language Models:
  Preference Learning from Process Feedback
Chengfeng Dou Zhi Jin Wenpin Jiao Haiyan Zhao Yongqiang Zhao Zhenwei Tao
The use of large language models in medical dialogue generation has garnered
significant attention, with a focus on improving response quality and fluency.
While previous studies have made progress in optimizing model performance for
single-round medical Q&A tasks, there is a need to enhance the model's
capability for multi-round conversations to avoid logical inconsistencies. To
address this, we propose an approach called preference learning from process
feedback~(PLPF), which integrates the doctor's diagnostic logic into LLMs. PLPF
involves rule modeling, preference data generation, and preference alignment to
train the model to adhere to the diagnostic process. Experimental results using
Standardized Patient Testing show that PLPF enhances the diagnostic accuracy of
the baseline model in medical conversations by 17.6%, outperforming traditional
reinforcement learning from human feedback. Additionally, PLPF demonstrates
effectiveness in both multi-round and single-round dialogue tasks, showcasing
its potential for improving medical dialogue generation.