mlsanityData to 7 Oct 2026

Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection

arXiv 2606.06748 ↗First posted 4 Jun 2026cs.CL cs.AI cs.LGcited by 2

On the map: Retrieval-augmented generation → Hallucination detection in RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models. Existing detection methods rely on flat similarity between generated answers and retrieved passages, ignoring structural relationships among evidence pieces and answer claims. We propose Evidence Graph Consistency (EGC), a framework that constructs a local evidence graph per response and computes five structural consistency measures as hallucination indicators. Evaluated on the full question answering split of RAGTruth across six LLMs (5,767 responses), EGC reveals a consistent model-family split: graph consistency features show the expected diagnostic direction for hallucinations in Llama-2 models but exhibit systematic reversal in GPT-4, GPT-3.5, and Mistral-7B. This reversal suggests qualitatively different hallucination patterns across model families and indicates that embedding-based graph consistency cannot serve as a model-independent hallucination detection signal.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 36 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

28 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation28 Aug
  2. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models31 Dec

Other references · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  9. 2023Mistral 7Bcited by 3,065
  10. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  11. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  12. 2015A large annotated corpus for learning natural language inferencecited by 1,837
14 more
  1. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  4. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  5. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  6. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  7. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  8. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  9. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  10. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  11. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  12. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 259
  13. 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
  14. 2024GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Frameworkcited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMscited by 0
  2. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models
  2. 2026Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation
  3. 2026Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph
  4. 2025Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment
  5. 2023SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
  6. 2026Unmasking Hallucinations: A Causal Graph-Attention Perspective on Factual Reliability in Large Language Models
  7. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
  8. 2025The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems