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ReGraM is a framework designed to improve the factual accuracy and consistency of Large Language Models (LLMs) in Medical Question Answering. It achieves this by constructing query-aligned subgraphs from a Knowledge Graph (KG) and constraining the LLM's reasoning exclusively within this localized region during the inference phase. This approach directly enhances the LLM's performance on a specific task (QA) by providing structured, relevant external knowledge and guiding its multi-hop reasoning.
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