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

• URL: http://arxiv.org/abs/2505.10143v1
• Date: Thu, 15 May 2025 10:17:35 GMT
• Title: GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs
• Authors: Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou, Jiaxing Zhang, Hua Wei,
• Abstract summary: This paper proposes GE-Chat, a knowledge Graph enhanced retrieval-augmented generation framework to provide evidence-based response generation.<n> Specifically, when the user uploads a material document, a knowledge graph will be created, which helps construct a retrieval-augmented agent.<n>We leverage Chain-of-Thought (CoT) logic generation, n-hop sub-graph searching, and entailment-based sentence generation to realize accurate evidence retrieval.
• Score: 6.3596531375179515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models are now key assistants in human decision-making processes. However, a common note always seems to follow: "LLMs can make mistakes. Be careful with important info." This points to the reality that not all outputs from LLMs are dependable, and users must evaluate them manually. The challenge deepens as hallucinated responses, often presented with seemingly plausible explanations, create complications and raise trust issues among users. To tackle such issue, this paper proposes GE-Chat, a knowledge Graph enhanced retrieval-augmented generation framework to provide Evidence-based response generation. Specifically, when the user uploads a material document, a knowledge graph will be created, which helps construct a retrieval-augmented agent, enhancing the agent's responses with additional knowledge beyond its training corpus. Then we leverage Chain-of-Thought (CoT) logic generation, n-hop sub-graph searching, and entailment-based sentence generation to realize accurate evidence retrieval. We demonstrate that our method improves the existing models' performance in terms of identifying the exact evidence in a free-form context, providing a reliable way to examine the resources of LLM's conclusion and help with the judgment of the trustworthiness.

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