KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
arxiv(2024)
摘要
Large Language Models (LLMs) have significantly advanced healthcare
innovation on generation capabilities. However, their application in real
clinical settings is challenging due to potential deviations from medical facts
and inherent biases. In this work, we develop an augmented LLM framework,
KG-Rank, which leverages a medical knowledge graph (KG) with ranking and
re-ranking techniques, aiming to improve free-text question-answering (QA) in
the medical domain. Specifically, upon receiving a question, we initially
retrieve triplets from a medical KG to gather factual information.
Subsequently, we innovatively apply ranking methods to refine the ordering of
these triplets, aiming to yield more precise answers. To the best of our
knowledge, KG-Rank is the first application of ranking models combined with KG
in medical QA specifically for generating long answers. Evaluation of four
selected medical QA datasets shows that KG-Rank achieves an improvement of over
18
realizes a 14
of KG-Rank.
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