mlsanityData to 7 Oct 2026

AI Hallucinations: A Misnomer Worth Clarifying

arXiv 2401.06796 ↗First posted 9 Jan 2024cs.CL cs.AIcited by 34

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Abstract (arXiv)

As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as "hallucination." However, with AI's increasing presence across various domains including medicine, concerns have arisen regarding the use of the term itself. In this study, we conducted a systematic review to identify papers defining "AI hallucination" across fourteen databases. We present and analyze definitions obtained across all databases, categorize them based on their applications, and extract key points within each category. Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature. We discuss implications of these and call for a more unified effort to bring consistency to an important contemporary AI issue that can affect multiple domains significantly.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 87 of its 334 references that are in the map. First posted on arXiv 9 Jan 2024.

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87 of 334 references.

References · 87

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  6. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  7. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  9. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  10. 2017Six Challenges for Neural Machine Translationcited by 334
  11. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  12. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
38 more
  1. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
  2. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 267
  3. 2023Mathematical Capabilities of ChatGPTcited by 263
  4. 2017Challenges in Data-to-Document Generationcited by 226
  5. 2022Factuality Enhanced Language Models for Open-Ended Text Generationcited by 200
  6. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  7. 2023A Categorical Archive of ChatGPT Failurescited by 167
  8. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  9. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  10. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 150
  11. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  12. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
  13. 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 137
  14. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  15. 2023Generative AIcited by 119
  16. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 108
  17. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  18. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  19. 2023Perspectives on Large Language Models for Relevance Judgmentcited by 84
  20. 2020Controlled Hallucinations: Learning to Generate Faithfully from Noisy Datacited by 83
  21. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  22. 2021The Factual Inconsistency Problem in Abstractive Text Summarization: A Surveycited by 79
  23. 2020Improved Natural Language Generation via Loss Truncationcited by 75
  24. 2021Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selectioncited by 75
  25. 2021Let there be a clock on the beach: Reducing Object Hallucination in Image Captioningcited by 74
  26. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 68
  27. 2022Mutual Information Alleviates Hallucinations in Abstractive Summarizationcited by 67
  28. 2020Multi-Fact Correction in Abstractive Text Summarizationcited by 66
  29. 2019Semantic Noise Matters for Neural Natural Language Generationcited by 64
  30. 2019Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generationcited by 62
  31. 2018Unsupervised Person Image Synthesis in Arbitrary Posescited by 55
  32. 2019Neural data-to-text generation: A comparison between pipeline and end-to-end architecturescited by 51
  33. 2022FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarizationcited by 49
  34. 2020Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inferencecited by 43
  35. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  36. 2021Prevent the Language Model from being Overconfident in Neural Machine Translationcited by 34
  37. 2023Trapping LLM Hallucinations Using Tagged Context Promptscited by 34
  38. 2023chatClimate: Grounding Conversational AI in Climate Sciencecited by 32

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