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AI Hallucinations: A Misnomer Worth Clarifying
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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
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“These articles primarily feature interviews with CEOs of big tech companies, who discuss future efforts to prevent "hallucination" in chatbots’ outputs.”
Background
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
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Linnebur, L. A., Syed, S. B., Girolamo, N., & Reid, J. W. (2025) · The journal of college science teaching · doi:10.1080/0047231x.2025.2535962 · Cited by 1
Abalo‐García, A., Ramírez, I., & Schiavi, E. (2025) · Expert Systems · doi:10.1111/exsy.70142 · Cited by 1
Assessment in Higher Education in the Era of Generative AI
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AI and the Law: Evaluating ChatGPT’s Performance in Legal Classification
Weichbroth, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13869-9_6 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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