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KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
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KG-Rank is an augmented LLM framework that integrates a medical knowledge graph (KG) with ranking and re-ranking techniques to improve the factuality of long-form question answering (QA). It identifies medical entities, retrieves related KG triples, and applies various ranking methods (Similarity, Answer Expansion, MMR) followed by re-ranking (MedCPT) to refine the information provided to the LLM during inference for answer generation. The primary goal is to enhance LLM performance by leveraging external knowledge from KGs at inference time.
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