Authors: Xiaohan Wang, Xiaoyan Yang, Yuqi Zhu, Yue Shen, Jian Wang, Peng Wei, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
Abstract: Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve
performance competitively with human experts across various medical benchmarks.
However, they still face challenges in making professional diagnoses akin to
physicians, particularly in efficiently gathering patient information and
reasoning the final diagnosis. To this end, we introduce the RuleAlign
framework, designed to align LLMs with specific diagnostic rules. We develop a
medical dialogue dataset comprising rule-based communications between patients
and physicians and design an alignment learning approach through preference
learning. Experimental results demonstrate the effectiveness of the proposed
approach. We hope that our work can serve as an inspiration for exploring the
potential of LLMs as AI physicians.
Source: http://arxiv.org/abs/2408.12579v1
