The Pitfalls of Defining Hallucination
Open accessAbstract
Despite impressive advances in Natural Language Generation (NLG) and Large Language Models (LLMs), researchers are still unclear about important aspects of NLG evaluation. To substantiate this claim, I examine current classifications of hallucination and omission in data-text NLG, and I propose a logic-based synthesis of these classfications. I conclude by highlighting some remaining limitations of all current thinking about hallucination and by discussing implications for LLMs.
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There is no universally accepted definition or classification of LM hallucinations.
“There is no universally accepted definition or classification of LM hallucinations (Ji, Lee, et al. 2023; van Deemter 2024; Y. Zhang et al. 2023)”
From Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024) · cites this paper as van Deemter 2024
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Cites
Grice, H. P. (1975) · Speech Acts · doi:10.1163/9789004368811_003 · Cited by 23,221 · see citing works
Levinson, S. C. (1983) · doi:10.1017/cbo9780511813313 · Cited by 5,137 · see citing works
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 · see citing works
Intention, Plans, and Practical Reason
Mccann, H., & Bratman, M. (1991) · Noûs · doi:10.2307/2215590 · Cited by 3,083 · see citing works
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works
Stanford Encyclopedia of Philosophy 2011
Loy, M. (2015) · doi:10.18665/sr.22379 · Cited by 1,767 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works
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 · see citing works
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776 · see citing works
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., et al. (2025) · Computational Linguistics · doi:10.1162/coli.a.16 · Cited by 534 · see citing works
Priest, G. (2002) · Springer eBooks · doi:10.1007/978-94-017-0460-1_4 · Cited by 514 · see citing works
Object Hallucination in Image Captioning
Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., & Saenko, K. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1437 · Cited by 420 · see citing works
Fitria, T. N. (2023) · ELT Forum: Journal of English Language Teaching · doi:10.15294/elt.v12i1.64069 · Cited by 283 · see citing works
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196 · see citing works
Not exactly: in praise of vagueness
(2010) · Choice Reviews Online · doi:10.5860/choice.48-0189 · Cited by 186 · see citing works
The Curious Case of Hallucinations in Neural Machine Translation
Raunak, V., Menezes, A., & Junczys-Dowmunt, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.92 · Cited by 142 · see citing works
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Harkous, H., Groves, I., & Saffari, A. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.218 · Cited by 68 · see citing works
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
Rotstein, N., Bensaïd, D., Brody, S., Ganz, R., & Kimmel, R. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00559 · Cited by 66 · see citing works
We’re Afraid Language Models Aren’t Modeling Ambiguity
Liu, A., Wu, Z., Michael, J., Suhr, A., West, P., Koller, A., Swayamdipta, S., Smith, N. A., & Choi, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.51 · Cited by 63 · see citing works
Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference
Dušek, O., & Kasner, Z. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.19 · Cited by 55 · see citing works
Dale, D. C., Voita, E., Barrault, L., & Costa‐jussà, M. R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.3 · Cited by 43 · see citing works
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation
Moramarco, F., Papadopoulos Korfiatis, A., Perera, M., Juric, D., Flann, J., Reiter, E., Belz, A., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.394 · Cited by 40 · see citing works
A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems
Thomson, C., & Reiter, E. (2020) · Proceedings of the 13th International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.22 · Cited by 38 · see citing works
Summarization is (Almost) DeadPreprint
Pu, X., Gao, M., & Wan, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.09558 · Cited by 33 · see citing works
Sakama, C., Caminada, M. W. A., & Herzig, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15675-5_25 · Cited by 28 · see citing works
Evaluating Generative Models for Graph-to-Text Generation
Yuan, S., & Färber, M. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_133 · Cited by 15 · see citing works
Entity-Based Semantic Adequacy for Data-to-Text Generation
Faille, J., Gatt, A., & Gardent, C. (2021) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.findings-emnlp.132 · Cited by 11 · see citing works
Editors’ Review and Introduction: Lying in Logic, Language, and Cognition
van Ditmarsch, H., Hendriks, P., & Verbrugge, R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12492 · Cited by 8 · see citing works
Barriers and enabling factors for error analysis in NLG research
van Miltenburg, E., Clinciu, M., Dušek, O., Gkatzia, D., Inglis, S., Leppänen, L., Mahamood, S., Schoch, S., Thomson, C., & Wen, L. (2023) · Northern European Journal of Language Technology · doi:10.3384/nejlt.2000-1533.2023.4529 · Cited by 5 · see citing works
A Survey of Recent Error Annotation Schemes for Automatically Generated Text
Huidrom, R., & Belz, A. (2022) · doi:10.18653/v1/2022.gem-1.33 · Cited by 5 · see citing works
Lorandi, M., & Belz, A. (2023) · Dublin City University Open Access Institutional Repository (Dublin City University) · doi:10.48550/arxiv.2308.09957 · Cited by 2 · see citing works
Flagged first, then by citations
3 of the 35 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Koehn, Philipp and Rebecca Knowles. 2017. Six challenges for neural machine translation. arXiv preprint arXiv:1706.03872. · arXiv
- Levinson, Stephen C. 1983. Pragmatics. Cambridge University Press.
Cited by
Sorted by citations
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Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024) · Journal of Legal Analysis · doi:10.1093/jla/laae003 · Cited by 288 · see citing works
Hallucination is Inevitable: An Innate Limitation of Large Language Models
Xu, Z., Jain, S., & Kankanhalli, M. (2024) · arXiv · doi:10.48550/arxiv.2401.11817 · Cited by 180 · see citing works
“This is because a formal definition of semantics in the real world is still an open problem [12, 58].”
BackgroundHow do judges use large language models? Evidence from Shenzhen
Liu, J. Z., & Li, X. (2024) · The Journal of Legal Analysis · doi:10.1093/jla/laae009 · Cited by 30 · see citing works
Evaluating large language models for accuracy incentivizes hallucinations
Kalai, A. T., Nachum, O., Vempala, S., & Zhang, E. (2026) · Nature · doi:10.1038/s41586-026-10549-w · Cited by 308 · see citing works
In Good Faith: Generative AI’s Plausible Falsehoods and Their Implications for Trust
Kodish, S. (2025) · Atlantic Journal of Communication · doi:10.1080/15456870.2025.2533224 · Cited by 2 · see citing works
Reasoning About Human Values in GitHub Issues: What Can a Large Language Model Reveal?
Mougouei, D., Haque, A., Nurwidyantoro, A., Mougouei, E., Fahmideh, M., Shahin, M., Dharmawan, A., Widodo, T. W., & Dam, H. K. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2594137 · Cited by 2 · see citing works
Generating Robotic Control Strategies With LLMs : Via Human–Robot Voice Interaction
Wang, X., Zhou, G., Hua, H., Tang, Z., & Cao, Y. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70372 · Cited by 1 · see citing works
Tamas, P., & Kamminga, L. (2026) · Journal of Information Literacy · doi:10.11645/20.1.847
Natural Language Processing for Social Good: contributing to research and society
Braun, D. (2025) · it - Information Technology · doi:10.1515/itit-2024-0095
Fang, X., Yuan, H. R., Li, H., Kou, J., Gu, C., Zhang, W., Duan, X., & Fang, Y. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189217 · Cited by 3 · see citing works
Nag, A., & Chaudhuri, D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7534-2.ch012
A Human-LLM study of value concerns in smart home app reviews
Mougouei, D., Pham, S. H., Azarnik, A., Mougouei, E., Nurwidyantoro, A., Arini, H. M., Trapsilawati, F., & A’yuninnisa, R. N. (2026) · Internet of Things · doi:10.1016/j.iot.2026.101982
Iterative conceptual query expansion for biomedical information retrieval
Zhou, C., Tu, X., Chen, G., He, T., Bai, X., & Liu, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133049
Exploring value concerns in social media issue reports: a human-LLM study of Bluesky and Mastodon
Mougouei, D., Azarnik, A., Mougouei, E., Pham, S. H., Kinchin, N., & Senewiratne, S. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2686827
Logical Misconceptions, Pragmatic Insufficiencies in LLMs and How to Fix Them
Asher, N. M., & Bhar, S. (2026) · Topoi · doi:10.1007/s11245-026-10459-6
- Authors
- Deemter, K. V.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Deemter, Kees van
- ORCID
- Deemter, Kees van
- Venue
- Computational Linguistics
- Publisher
- MIT Press
- Published
- 2024
- Volume
- 50
- Issue
- 2
- Pages
- 807-816
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W4391017628
- Cited by
- 13
- References
- 35
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 3
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Computational Linguistics (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- hallucinations, omission, natural language generation evaluation, large language models
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