mlsanityData to 14 Oct 2026

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

arXiv 2505.10143 ↗First posted 15 May 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

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.

Key facts. Cited by 1 paper 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.

Builds on

17 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering19 Jul
  2. 2024Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition23 Jan

Other references · 15

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  3. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  4. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 688
  5. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 315
  6. 2024Advancing LLM Reasoning Generalists with Preference Treescited by 192
  7. 2023BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questionscited by 125
  8. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
  9. 2023Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewritescited by 61
  10. 2023Open-TI: Open Traffic Intelligence with Augmented Language Modelcited by 38
  11. 2023PDFTriage: Question Answering over Long, Structured Documentscited by 29
  12. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMscited by 27
3 more
  1. 2024LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentationcited by 23
  2. 2024LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generationcited by 8
  3. 2024Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intentscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignmentcited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2026Why Retrieval-Augmented Generation Fails: A Graph Perspective
  2. 2026MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
  3. 2026Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering
  4. 2026BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs
  5. 2026SearchAtlas: Analyzing Agentic Search Strategies via Evidential Query Graphs
  6. 2026Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning
  7. 2025FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation
  8. 2025Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval