Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large
  Language Models
Xinke Jiang Ruizhe Zhang Yongxin Xu Rihong Qiu Yue Fang Zhiyuan Wang Jinyi Tang Hongxin Ding Xu Chu Junfeng Zhao Yasha Wang
We explore how the rise of Large Language Models (LLMs) significantly impacts
task performance in the field of Natural Language Processing. We focus on two
strategies, Retrieval-Augmented Generation (RAG) and Fine-Tuning (FT), and
propose the Hypothesis Knowledge Graph Enhanced (HyKGE) framework, leveraging a
knowledge graph to enhance medical LLMs. By integrating LLMs and knowledge
graphs, HyKGE demonstrates superior performance in addressing accuracy and
interpretability challenges, presenting potential applications in the medical
domain. Our evaluations using real-world datasets highlight HyKGE's superiority
in providing accurate knowledge with precise confidence, particularly in
complex and difficult scenarios. The code will be available until published.