Large Language Models With Holistically Thought Could Be Better Doctors

Yixuan Weng, Bin Li, Fei Xia, Minjun Zhu, Bin Sun, Shizhu He, Shengping Liu, Kang Liu, Shutao Li, Jun Zhao

Abstract: The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the success of large language models (LLMs) in complex reasoning tasks in various fields, such as mathematics, logic, and commonsense QA, they still need to improve with the increased complexity and specialization of the medical field. This is because medical CQA tasks require not only strong medical reasoning, but also the ability to think broadly and deeply. In this paper, to address these challenges in medical CQA tasks that need to be considered and understood in many aspects, we propose the Holistically Thought (HoT) method, which is designed to guide the LLMs to perform the diffused and focused thinking for generating high-quality medical responses. The proposed HoT method has been evaluated in three different medical CQA datasets containing English and Chinese languages. The extensive experimental results show that our method can produce more correct, professional, and considerate answers than several SOTA methods, manifesting its effectiveness.