mlsanityData to 7 Oct 2026

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

arXiv 2401.01313 ↗First posted 2 Jan 2024cs.CLcited by 266

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Cited by 43 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (Lewis et al, 2021), Knowledge Retrieval (Varshney et al,2023), CoNLI (Lei et al, 2023), and CoVe (Dhuliawala et al, 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.

Key facts. Cited by 266 papers in the map (data through 7 Oct 2026). Builds on 37 of its 50 references that are in the map. First posted on arXiv 2 Jan 2024.

Builds on

37 of 50 references.

References · 37

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  6. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  7. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  8. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  9. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  10. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  11. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  12. 2020Large-Scale Adversarial Training for Vision-and-Language Representation Learningcited by 327
25 more
  1. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervisioncited by 318
  2. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  3. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  4. 2022Prompting GPT-3 To Be Reliablecited by 258
  5. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
  6. 2023Fine-tuning Language Models for Factualitycited by 219
  7. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  8. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
  9. 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 106
  10. 2023DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedbackcited by 99
  11. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  12. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 74
  13. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  14. 2022RHO (ρ): Reducing Hallucination in Open-domain Dialogues with Knowledge Groundingcited by 47
  15. 2023Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectificationcited by 38
  16. 2023Trapping LLM Hallucinations Using Tagged Context Promptscited by 34
  17. 2023Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinationscited by 34
  18. 2023Teaching Language Models to Hallucinate Less with Synthetic Taskscited by 31
  19. 2022Information-Theoretic Text Hallucination Reduction for Video-grounded Dialoguecited by 18
  20. 2021Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Saliencecited by 16
  21. 2023textitDial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuningcited by 9
  22. 2023Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generationcited by 7
  23. 2023Predicting Text Preference Via Structured Comparative Reasoningcited by 6
  24. 2023Hallucination Augmented Recitations for Language Modelscited by 5
  25. 2023Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 99; 2025: 113; 2026: 54.

266 papers.

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  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  2. 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 451
  3. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  5. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
  6. 2024Language agents achieve superhuman synthesis of scientific knowledgecited by 121
  7. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  8. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 110
  9. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  10. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  11. 2024Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluationcited by 79
  12. 2024LLMs Will Always Hallucinate, and We Need to Live With Thiscited by 77
38 more
  1. 2024Unified Hallucination Detection for Multimodal Large Language Modelscited by 75
  2. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  3. 2024Factuality of Large Language Models: A Surveycited by 64
  4. 2024We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMscited by 61
  5. 2025SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Modelscited by 61
  6. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generationcited by 59
  7. 2025Contextual Agent Security: A Policy for Every Purposecited by 55
  8. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 54
  9. 2024MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Modelscited by 51
  10. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  11. 2024On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Modelscited by 42
  12. 2025CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countriescited by 40
  13. 2024A Survey on Large Language Model Hallucination via a Creativity Perspectivecited by 38
  14. 2024Exploring Prosocial Irrationality for LLM Agents: A Social Cognition Viewcited by 38
  15. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 38
  16. 2024Debiasing Multimodal Large Language Models via Penalization of Language Priorscited by 36
  17. 2024KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detectioncited by 29
  18. 2024Can Knowledge Editing Really Correct Hallucinations?cited by 28
  19. 2025Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Reportcited by 28
  20. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methodscited by 27
  21. 2025AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decodingcited by 26
  22. 2024Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Modelscited by 24
  23. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  24. 2024Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Surveycited by 23
  25. 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoningcited by 23
  26. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systemscited by 22
  27. 2024ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaborationcited by 22
  28. 2025Sparks of Science: Hypothesis Generation Using Structured Paper Datacited by 22
  29. 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
  30. 2025Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Reportcited by 21
  31. 2024WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Drivingcited by 19
  32. 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Modelscited by 19
  33. 2024CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Modelscited by 18
  34. 2024Correctness is not Faithfulness in RAG Attributionscited by 18
  35. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  36. 2024Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulnesscited by 17
  37. 2024HonestLLM: Toward an Honest and Helpful Large Language Modelcited by 17
  38. 2025A Survey on Large Language Models for Automated Planningcited by 17

50 most cited of 266 All 266 (JSON)

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  1. 2024Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
  2. 2024THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
  3. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  5. 2026Evaluating Hallucinations in Domain-Adapted Large Language Models
  6. 2024Banishing LLM Hallucinations Requires Rethinking Generalization
  7. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
  8. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answering