mlsanityData to 13 Oct 2026

An Audit on the Perspectives and Challenges of Hallucinations in NLP

arXiv 2404.07461 ↗First posted 11 Apr 2024cs.CL cs.AIcited by 17

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Abstract (arXiv)

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term `hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Builds on 59 of its 123 references that are in the map. First posted on arXiv 11 Apr 2024.

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59 of 123 references.

References · 59

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  3. 2020Recipes for building an open-domain chatbotcited by 593
  4. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 526
  5. 2023A Survey of Hallucination in Large Foundation Modelscited by 367
  6. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 352
  7. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  8. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 169
  9. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  10. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  11. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  12. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
38 more
  1. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  2. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
  3. 2023LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructionscited by 124
  4. 2021Entity-level Factual Consistency of Abstractive Text Summarizationcited by 118
  5. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 108
  6. 2023Enhancing Uncertainty-Based Hallucination Detection with Stronger Focuscited by 99
  7. 2023SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistencycited by 90
  8. 2020Controlled Hallucinations: Learning to Generate Faithfully from Noisy Datacited by 84
  9. 2021Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selectioncited by 76
  10. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  11. 2022Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingcited by 71
  12. 2022Mutual Information Alleviates Hallucinations in Abstractive Summarizationcited by 67
  13. 2019Domain Robustness in Neural Machine Translationcited by 65
  14. 2022Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agentscited by 62
  15. 2022Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generationcited by 62
  16. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  17. 2022RHO (ρ): Reducing Hallucination in Open-domain Dialogues with Knowledge Groundingcited by 47
  18. 2022Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformercited by 45
  19. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answeringcited by 43
  20. 2023"According to ...": Prompting Language Models Improves Quoting from Pre-Training Datacited by 42
  21. 2023A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detectioncited by 42
  22. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  23. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  24. 2023Diving Deep into Modes of Fact Hallucinations in Dialogue Systemscited by 32
  25. 2019Pushing the Limits of Low-Resource Morphological Inflectioncited by 29
  26. 2019How Decoding Strategies Affect the Verifiability of Generated Textcited by 27
  27. 2023Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucinationcited by 27
  28. 2023mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinationscited by 24
  29. 2023CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQLcited by 23
  30. 2023Models See Hallucinations: Evaluating the Factuality in Video Captioningcited by 19
  31. 2022Information-Theoretic Text Hallucination Reduction for Video-grounded Dialoguecited by 18
  32. 2023Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articlescited by 16
  33. 2023Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generationcited by 13
  34. 2024From Melting Pots to Misrepresentations: Exploring Harms in Generative AIcited by 13
  35. 2022Contrastive Error Attribution for Finetuned Language Modelscited by 12
  36. 2023The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysiscited by 12
  37. 2021CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarizationcited by 11
  38. 2023Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lenscited by 9

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Citing papers per year: 2024: 1; 2025: 9; 2026: 7.

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  1. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  2. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 25
  3. 2025HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistantscited by 10
  4. 2025Evaluating the Evaluators: Are readability metrics good measures of readability?cited by 6
  5. 2025A Unified Definition of Hallucination: It's The World Model, Stupid!cited by 6
  6. 2025Identifying and Mitigating the Influence of the Prior Distribution in Large Language Modelscited by 4
  7. 2025Catch Me if You Search: When Contextual Web Search Results Affect the Detection of Hallucinationscited by 3
  8. 2025Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answerscited by 3
  9. 2026What if AI systems weren't chatbots?cited by 2
  10. 2026HalluWorld: A Controlled Benchmark for Hallucination via Reference World Modelscited by 2
  11. 2026Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicitycited by 1
  12. 2025TUM-MiKaNi at SemEval-2025 Task 3: Towards Multilingual and Knowledge-Aware Non-factual Hallucination Identificationcited by 0
5 more
  1. 2025NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Researchcited by 0
  2. 2026KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledgecited by 0
  3. 2026Explain Like I'm 5 or Whatever I Choose: Evaluating the Potential for Interactive Complexity in Language Model Responsescited by 0
  4. 2026AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviewscited by 0
  5. 2026Does Finetuning with Scientific Data Increase Hallucinations? A Multi-domain Factuality Evaluation of LLMscited by 0