mlsanityData to 7 Oct 2026

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models

arXiv 2401.00396 ↗First posted 31 Dec 2023cs.CLcited by 272

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

Abstract (arXiv)

Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under RAG, it is important to create benchmark datasets that can measure the extent of hallucination. This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains and tasks within the standard RAG frameworks for LLM applications. RAGTruth comprises nearly 18,000 naturally generated responses from diverse LLMs using RAG. These responses have undergone meticulous manual annotations at both the individual cases and word levels, incorporating evaluations of hallucination intensity. We not only benchmark hallucination frequencies across different LLMs, but also critically assess the effectiveness of several existing hallucination detection methodologies. Furthermore, we show that using a high-quality dataset such as RAGTruth, it is possible to finetune a relatively small LLM and achieve a competitive level of performance in hallucination detection when compared to the existing prompt-based approaches using state-of-the-art large language models such as GPT-4.

Key facts. Cited by 272 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 34 of its 47 references that are in the map. First posted on arXiv 31 Dec 2023.

Builds on

34 of 47 references.

References · 34

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
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  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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  12. 2023LIMA: Less Is More for Alignmentcited by 1,080
22 more
  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  3. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  4. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 416
  5. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
  6. 2021A Survey on Automated Fact-Checkingcited by 376
  7. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  8. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
  9. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 349
  10. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  11. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
  12. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  13. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  14. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  15. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  16. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  17. 2023WiCE: Real-World Entailment for Claims in Wikipediacited by 118
  18. 2023Do Language Models Know When They're Hallucinating References?cited by 115
  19. 2021Pretrained Language Models for Text Generation: A Surveycited by 95
  20. 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 75
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 42; 2025: 101; 2026: 129.

272 papers.

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  1. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
  2. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 116
  3. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domaincited by 113
  4. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  5. 2024MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Modelscited by 84
  6. 2024Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluationcited by 72
  7. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 69
  8. 2025Evaluating Step-by-step Reasoning Traces: A Surveycited by 62
  9. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 55
  10. 2025VideoRAG: Retrieval-Augmented Generation over Video Corpuscited by 55
  11. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systemscited by 50
  12. 2024Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrievalcited by 45
38 more
  1. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  2. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Frameworkcited by 39
  3. 2024Direct Judgement Preference Optimizationcited by 33
  4. 2025LettuceDetect: A Hallucination Detection Framework for RAG Applicationscited by 32
  5. 2024Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applicationscited by 31
  6. 2026From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agentscited by 29
  7. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  8. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontierscited by 26
  9. 2024FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMscited by 24
  10. 2025Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancementcited by 24
  11. 2025Steering MoE LLMs via Expert (De)Activationcited by 23
  12. 2025Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settingscited by 22
  13. 2025Benchmarking LLM Faithfulness in RAG with Evolving Leaderboardscited by 19
  14. 2024The Open Source Advantage in Large Language Models (LLMs)cited by 18
  15. 2025Hallucination Detection in LLMs with Topological Divergence on Attention Graphscited by 18
  16. 2025GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluationcited by 18
  17. 2024LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagationcited by 17
  18. 2025MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Themcited by 16
  19. 2024Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspectivecited by 15
  20. 2024TLDR: Token-Level Detective Reward Model for Large Vision Language Modelscited by 15
  21. 2024Improving Model Factuality with Fine-grained Critique-based Evaluatorcited by 15
  22. 2025FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documentscited by 15
  23. 2026AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agentscited by 15
  24. 2024Halu-J: Critique-Based Hallucination Judgecited by 14
  25. 2024MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systemscited by 14
  26. 2025FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Datacited by 14
  27. 2025Refine Knowledge of Large Language Models via Adaptive Contrastive Learningcited by 14
  28. 2025(Im)possibility of Automated Hallucination Detection in Large Language Modelscited by 14
  29. 2025HalluciNot: Hallucination Detection Through Context and Common Knowledge Verificationcited by 13
  30. 2025When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQAcited by 13
  31. 2024An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Modelscited by 12
  32. 2024ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosurescited by 12
  33. 2025Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifierscited by 12
  34. 2026TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectoriescited by 12
  35. 2025ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Draftingcited by 11
  36. 2025Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaningcited by 10
  37. 2025Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questionscited by 10
  38. 2026Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?cited by 10

50 most cited of 272 All 272 (JSON)

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  1. 2025Probabilistic distances-based hallucination detection in LLMs with RAG
  2. 2025HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  4. 2025MetaRAG: Metamorphic Testing for Hallucination Detection in RAG Systems
  5. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  6. 2026Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
  7. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
  8. 2025HALT-RAG: A Task-Adaptable Framework for Hallucination Detection with Calibrated NLI Ensembles and Abstention