KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

Understand

Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine.

• However, the application of LLMs in real clinical settings remains challenging due to the lack of factual consistency in the generated content.
• In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain.
• Specifically, when receiving a question, KG-Rank automatically identifies medical entities within the question and retrieves the related triples from the medical KG to gather factual information.