mlsanityData to 7 Oct 2026

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

arXiv 2311.05232 ↗First posted 9 Nov 2023cs.CLcited by 1,582

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Cited by 240 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible yet nonfactual content. This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval (IR) systems and has attracted intensive research to detect and mitigate such hallucinations. Given the open-ended general-purpose attributes inherent to LLMs, LLM hallucinations present distinct challenges that diverge from prior task-specific models. This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations. In this survey, we begin with an innovative taxonomy of hallucination in the era of LLM and then delve into the factors contributing to hallucinations. Subsequently, we present a thorough overview of hallucination detection methods and benchmarks. Our discussion then transfers to representative methodologies for mitigating LLM hallucinations. Additionally, we delve into the current limitations faced by retrieval-augmented LLMs in combating hallucinations, offering insights for developing more robust IR systems. Finally, we highlight the promising research directions on LLM hallucinations, including hallucination in large vision-language models and understanding of knowledge boundaries in LLM hallucinations.

Key facts. Cited by 1,582 papers in the map (data through 7 Oct 2026). Builds on 214 of its 287 references that are in the map. First posted on arXiv 9 Nov 2023.

Builds on

214 of 287 references.

References · 214

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38 more
  1. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
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50 most cited of 214 All 214 (JSON)

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Citing papers per year: 2023: 14; 2024: 341; 2025: 680; 2026: 547.

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  3. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
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  6. 2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmarkcited by 362
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  8. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  9. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 242
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  11. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 217
  12. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
38 more
  1. 2024An Introduction to Vision-Language Modelingcited by 175
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  4. 2026Towards End-to-End Automation of AI Researchcited by 150
  5. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
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  7. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
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  11. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  12. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
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  14. 2024IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decodingcited by 118
  15. 2025CollabLLM: From Passive Responders to Active Collaboratorscited by 117
  16. 2024AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformerscited by 116
  17. 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Modelscited by 114
  18. 2024What Large Language Models Know and What People Think They Knowcited by 109
  19. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 107
  20. 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
  21. 2025MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphscited by 105
  22. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 100
  23. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  24. 2024TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuningcited by 99
  25. 2024MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Enginescited by 98
  26. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 96
  27. 2024CRAG -- Comprehensive RAG Benchmarkcited by 94
  28. 2024Learning Dynamics of LLM Finetuningcited by 93
  29. 2024ContextCite: Attributing Model Generation to Contextcited by 93
  30. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
  31. 2024LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristicscited by 92
  32. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  33. 2025VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Usecited by 92
  34. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  35. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  36. 2024WavChat: A Survey of Spoken Dialogue Modelscited by 88
  37. 2025HalluLens: LLM Hallucination Benchmarkcited by 88
  38. 2025VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuningcited by 86

50 most cited of 1,582 All 1,582 (JSON)

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  1. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
  2. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  3. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
  4. 2025MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
  5. 2024THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
  6. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
  7. 2026Evaluating Hallucinations in Domain-Adapted Large Language Models
  8. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild