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Retrieval-Augmented Question Answering System Based on Large Language Models and Knowledge Graphs

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The proposed framework significantly reduces hallucinations in LLM-generated medical responses, enhancing accuracy.
On the test set, the average BLEU score improved from 0.0102 to 0.0157, showcasing performance gains.
A knowledge graph-based approach was developed for the dermatology domain using the GLM-4 model for knowledge extraction.
This methodology could serve as a foundation for safer medical AI systems in various medical specialties.

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This framework reduces hallucination in LLM responses for dermatology, suggesting enhanced reliability.

Z. Y. Wang (2025) studied this question.

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