---
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"
doi: "10.24963/ijcai.2025/1256"
url: "https://ijcai.org/proceedings/2025/1256"
pdf: "https://ijcai.org/proceedings/2025/1256.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11030-11033"
track: "Demo Track"
keywords:
  - "AI Ethics, Trust, Fairness: ETF: Trustworthy AI"
  - "Natural Language Processing: NLP: Applications"
  - "Natural Language Processing: NLP: Information extraction"
  - "Natural Language Processing: NLP: Interpretability and analysis of models for NLP"
---

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

Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou, Jiaxing Zhang, Hua Wei

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11030-11033. DOI: [10.24963/ijcai.2025/1256](https://doi.org/10.24963/ijcai.2025/1256)

## Abstract

Large Language Models (LLMs) have become integral to human decision-making processes. However, their outputs are not always reliable, often requiring users to assess the accuracy of the information provided manually. This issue is exacerbated by hallucinated responses, which are frequently presented with convincing but incorrect explanations, leading to trust concerns among users. To address this challenge, we propose GE-Chat, a knowledge Graph-enhanced retrieval-augmented generation framework designed to deliver Evidence-based responses. Specifically, when users upload a document, GE-Chat constructs a knowledge graph to support a retrieval-augmented agent, enriching the agent's responses with external knowledge beyond its training data. We further incorporate Chain-of-Thought (CoT) reasoning, n-hop subgraph searching, and entailment-based sentence generation to ensure accurate evidence retrieval. Experimental results demonstrate that our approach improves the ability of existing models to identify precise evidence in free-form contexts, offering a reliable mechanism for verifying LLM-generated conclusions and enhancing trustworthiness.