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

arXiv: https://arxiv.org/abs/2505.10143 · HTML: https://mlsanity.com/paper/2505.10143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 15 May 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 17 of 17)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [Advancing LLM Reasoning Generalists with Preference Trees](https://mlsanity.com/paper/2404.02078/) (2024)
- [BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions](https://mlsanity.com/paper/2308.09936/) (2023)
- [Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey](https://mlsanity.com/paper/2503.15850/) (2025)
- [Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites](https://mlsanity.com/paper/2312.01701/) (2023)
- [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://mlsanity.com/paper/2407.13998/) (2024)
- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [PDFTriage: Question Answering over Long, Structured Documents](https://mlsanity.com/paper/2309.08872/) (2023)
- [Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs](https://mlsanity.com/paper/2402.10612/) (2024)
- [LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation](https://mlsanity.com/paper/2407.00994/) (2024)
- [Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition](https://mlsanity.com/paper/2401.12599/) (2024)
- [LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation](https://mlsanity.com/paper/2407.15351/) (2024)
- [Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intents](https://mlsanity.com/paper/2406.11047/) (2024)

## Built on by (most cited first, 1 of 1)

- [Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment](https://mlsanity.com/paper/2601.17329/) (2026)

## Abstract (arXiv)

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.