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October 16, 2025Open Access

GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs

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GE-Chat improves the reliability of outputs from large language models by integrating knowledge graphs for better response accuracy.
The method enhances evidence retrieval through chain-of-thought logic generation and n-hop sub-graph searching.
A knowledge graph is created from user-uploaded documents, allowing for evidence-based responses that go beyond the model's training corpus.
This approach offers a reliable mechanism for evaluating the trustworthiness of LLM conclusions and mitigating misinformation.

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This framework enhances evidence-based response generation in large language models, addressing hallucination issues.

Da et al. (2025) studied this question.

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