The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index, and 1 are not checked yet.
AI Hallucinations: A Misnomer Worth Clarifying
Open accessAbstract
As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon often termed 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 from 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 the implications of these findings and call for a more unified effort to bring consistency to this important contemporary AI issue, which can significantly affect multiple domains.
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“First, they hallucinate, i.e., produce plausiblesounding but false or fallacious statements (Kalai and Vempala, 2024;Maleki et al., 2024).”
From There is no such thing as conscious artificial intelligence (2025) · cites this paper as Maleki et al., 2024
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“the reality is that generative AI can produce incorrect [Longwell et al., 2024] or hallucinated content [Maleki et al., 2024].”
From How AI Impacts Skill Formation (2026) · cites this paper as Maleki et al., 2024
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · cited 2×
“Ji et al. [10], from the computer science perspective (in ACM Computing Surveys), rationalized the use of the term "hallucination" as "an unreal perception that feels real" by drawing from Blom’s definition — "a percept, experienced by a waking individual, in the absence of an appropriate stimulus…”
BackgroundLarge language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
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On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185
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Challenges in Data-to-Document Generation
Wiseman, S., Shieber, S., & Rush, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1239 · Cited by 524
A Categorical Archive of ChatGPT Failures
Borji, A. (2023) · doi:10.21203/rs.3.rs-2895792/v1 · Cited by 514
Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?
Currie, G. M. (2023) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2023.04.008 · Cited by 283
ChatGPT: these are not hallucinations – they’re fabrications and falsifications
Emsley, R. (2023) · Schizophrenia · doi:10.1038/s41537-023-00379-4 · Cited by 258
Gravel, J., D’Amours-Gravel, M., & Osmanlliu, E. (2023) · Mayo Clinic Proceedings: Digital Health · doi:10.1016/j.mcpdig.2023.05.004 · Cited by 222 · cited 1×
“These articles primarily feature interviews with CEOs of big tech companies, who discuss future efforts to prevent "hallucination" in chatbots’ outputs.”
BackgroundDeep learning for image inpainting: A survey
Xiang, H., Zou, Q., Nawaz, M. A., Huang, X., Zhang, F., & Yu, H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109046 · Cited by 221
Unsupervised Person Image Synthesis in Arbitrary Poses
Pumarola, A., Agudo, A., Sanfeliu, A., & Moreno-Noguer, F. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00899 · Cited by 158
To ChatGPT or not to ChatGPT? The Impact of Artificial Intelligence on Academic Publishing
Curtis, N. C., & ChatGPT (2023) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000003852 · Cited by 138
Multi-Fact Correction in Abstractive Text Summarization
Dong, Y., Wang, S., Gan, Z., Cheng, Y., Cheung, J. C. K., & Liu, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.749 · Cited by 115
(2018) · Cited by 115
Reducing Quantity Hallucinations in Abstractive Summarization
Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99
Artificial intelligence-based text generators in hepatology: ChatGPT is just the beginning
Ge, J., & Lai, J. C. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000097 · Cited by 83
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Filippova, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.76 · Cited by 79
ChatGPT in ophthalmology: the dawn of a new era?
Ting, D. S. J., Tan, T. F., & Ting, D. S. W. (2023) · Eye · doi:10.1038/s41433-023-02619-4 · Cited by 70
Cognitive Mirage: A Review of Hallucinations in Large Language Models
Ye, H., Liu, T., Zhang, A., Hua, W., & Jia, W. (2023) · arXiv · doi:10.48550/arxiv.2309.06794 · Cited by 61
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
Braunegg, A., Chakraborty, A., Krumdick, M., Lape, N., Leary, S., Manville, K., Merkhofer, E., Strickhart, L., & Walmer, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_3 · Cited by 57
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning
Biten, A. F., Gomez, L., & Karatzas, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00253 · Cited by 43
The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and HallucinationPreprint
Li, Z. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.14347 · Cited by 36
Artificial Intelligence and the Simulationists
Rodgers, D. L., Needler, M., Robinson, A., Barnes, R. L., Brosche, T. A., Hernandez, J., Poore, J. A., VandeKoppel, P., & Ahmed, R. A. (2023) · Simulation in Healthcare The Journal of the Society for Simulation in Healthcare · doi:10.1097/sih.0000000000000747 · Cited by 36
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Prevent the Language Model from being Overconfident in Neural Machine Translation
Miao, M., Meng, F., Liu, Y., Zhou, X., & Zhou, J. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.268 · Cited by 30
Karakas, C., Brock, D. C., & Lakhotia, A. (2023) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2023.08.035 · Cited by 28
Sriwastwa, A., Ravi, P., Emmert, A., Chokshi, S., Kondor, S., Dhal, K., Patel, P., Chepelev, L. L., Rybicki, F. J., & Gupta, R. (2023) · 3D Printing in Medicine · doi:10.1186/s41205-023-00186-8 · Cited by 28
False Responses From Artificial Intelligence Models Are Not Hallucinations
Østergaard, S. D., & Nielbo, K. L. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad068 · Cited by 27
AI Chatbots: Threat or Opportunity?
Bryant, A. (2023) · Informatics · doi:10.3390/informatics10020049 · Cited by 19
Mahon, J., Namee, B. M., & Becker, B. A. (2023) · Conference on United Kingdom & Ireland Computing Education Research (UKICER) · doi:10.1145/3610969.3610982 · Cited by 15
Faithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer
Lyu, Y., Zhu, C., Xu, T., Yin, Z., & Chen, E. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557319 · Cited by 11
Research works printed but not linked: 2
- 10.18653/v1/W17-3204 · DOI · Paper page
- Press, 2023, Celebrating 80 years of hallucinating about artificial intelligence
Cited by
Sorted by citations
Zhang, Q., Zuo, J., & Yang, S. (2025) · Journal of Knowledge Management · doi:10.1108/jkm-10-2024-1198 · Cited by 108
Shi, J., Liu, W., & Hu, K. (2025) · Behavioral Sciences · doi:10.3390/bs15050705 · Cited by 77
AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers
Wang, W.-F., Lu, C.-T., Ponsa i Campanyà, N., Chen, B.-Y., & Chen, M. Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714148 · Cited by 50
Experiential Narratives in Marketing: A Comparison of Generative AI and Human Content
Wen, Y., & Laporte, S. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241297973 · Cited by 62
Machine Learning and Artificial Intelligence Applications in Soil Science
Minasny, B., & McBratney, A. B. (2025) · European Journal of Soil Science · doi:10.1111/ejss.70093 · Cited by 45
Metadata
- Authors
- Maleki, N., Padmanabhan, B., & Dutta, K.
- Affiliations
- University of South Florida (US)
- University of Maryland, College Park (US)
- Venue
- 2024 IEEE Conference on Artificial Intelligence (CAI)
- Publisher
- IEEE
- Published
- 2024-06-25
- Pages
- 133-138
- OpenAlex
- W4401110692
- Cited by
- 194
- References
- 31
- Citations by year
- 2026: 78 · 2025: 92 · 2024: 23
- FWCI
- 56.96
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Conference on Artificial Intelligence (CAI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- AI hallucination, terminology, large language models, text generation, artificial intelligence in healthcare, terminology standardization
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