On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
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Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, Siva Reddy. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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LLMs are capable of producing falsehoods or bias.
“LLMs are also capable of producing falsehoods [94] or bias [95].”
From Deanthropomorphising NLP: Can a language model be conscious? (2024) · cites this paper as [94]
Prior work has shown that retrieval-augmented models generate text that either includes additional information outside the retrieved documents, ignores the documents altogether, or even contradicts the documents.
“Prior work has shown that retrieval-augmented models generate text that either includes additional information outside the retrieved documents (Dziri et al., 2022), ignores the documents altogether (Krishna et al., 2021), or even contradicts the documents (Longpre et al., 2021).”
From RARR: Researching and revising what language models say, using language models (2023) · cites this paper as Dziri et al., 2022
Hallucination issues in healthcare chatbots arise when responses appear factually accurate but lack validity.
“Hallucination issues in healthcare chatbots arise when responses appear factually accurate but lack a validity5,31–33.”
From Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI (2024) · cites this paper as [32]
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Explicit Syntactic Guidance for Neural Text Generation
Li, Y., Cui, L., Yan, J., Yin, Y., Bi, W., Shi, S., & Zhang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.788 · Cited by 8 · see citing works
Fayyaz, Y., Almehmadi, A., & El‐Khatib, K. (2024) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2024.301751 · Cited by 6 · see citing works
Vakharia, P., Kufeldt, A., Meyers, M., Lane, I., & Gilpin, L. H. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-71170-1_23 · Cited by 5 · see citing works
Generative AI Decision-Making Attributes in Complex Health Services: A Rapid Review
Doreswamy, N., & Horstmanshof, L. (2025) · Cureus · doi:10.7759/cureus.78257 · Cited by 9 · see citing works
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Subramonian, A., Yuan, X., Daumé, H., & Blodgett, S. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.202 · Cited by 7 · see citing works
Chinese Chat Room: AI Hallucinations, Epistemology and Cognition
Šekrst, K. (2024) · Studies in Logic Grammar and Rhetoric · doi:10.2478/slgr-2024-0029 · Cited by 4 · see citing works
Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
Vitale, A., Mastropaolo, A., Oliveto, R., Penta, M. D., & Scalabrino, S. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00033 · Cited by 8 · see citing works
Towards Faithful Dialogues via Focus Learning
Deng, Y., Zhang, X., Huang, H., & Hu, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.250 · Cited by 4 · see citing works
Exploring In-Context Learning for Knowledge Grounded Dialog Generation
Chen, Q., Wu, W., & Li, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.675 · Cited by 4 · see citing works
Zaman, I.-R., & Trăușan-Matu, Ș. (2024) · Information · doi:10.3390/info15030123 · Cited by 5 · see citing works
Correcting Factuality Hallucination in Complaint Large Language Model via Entity-Augmented
Kang, J., Pan, W., Zhang, T., Wang, Z., Yang, S., Wang, Z., Wang, J., & Niu, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650208 · Cited by 4 · see citing works
Sun, B., Li, Y., Mi, F., Bie, F., Li, Y., & Li, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.148 · Cited by 3 · see citing works
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
Vu, T.-T., He, X., Haffari, G., & Shareghi, E. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.7 · Cited by 6 · see citing works
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Litschko, R., Müller-Eberstein, M., van der Goot, R., Weber, L., & Plank, B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.14 · Cited by 6 · see citing works
Conditioning on Dialog Acts improves Empathy Style Transfer
Qu, R., Ungar, L., & Sedoc, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.884 · Cited by 3 · see citing works
Joseph, T., & Kenneth, M. H. (2024) · East African Journal of Information Technology · doi:10.37284/eajit.7.1.2111 · Cited by 4 · see citing works
Liu, H., Yin, S., Hu, X., Deng, M., Yang, X., & Xu, G. (2025) · Applied Sciences · doi:10.3390/app15169012 · Cited by 3 · see citing works
Šekrst, K. (2025) · Springer eBooks · doi:10.1007/978-3-032-05562-0_12 · Cited by 2 · see citing works
Fedoriv, Y., Shuhai, A., & Pirozhenko, I. (2023) · Humanities science current issues · doi:10.24919/2308-4863/66-3-20 · Cited by 2 · see citing works
Are You Sure About That: Eliciting Natural Language Confidence Indicators from Chatbots
Toney, A., & Singh, L. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3688318 · Cited by 2 · see citing works
Graph-Based Grounding in a Conversational Clinical Decision Support System
Barham, S., Kelbaugh, M. T., Reddy, A., Ramsden, D., Chandler, C., Tobin-Williams, A., Elhadad, B. S., Cooper, G. R., & Alexander, K. (2024) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa64256.2024.00014 · Cited by 2 · see citing works
Investigation of the Inference Capabilities and Memorization of Pre-trained Language Models
Sakai, Y., Kamigaito, H., Hayashi, K., & Watanabe, T. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1427 · Cited by 2 · see citing works
AI in the city: impact assessment of artificial intelligence uses in earth observation
Šćepanović, S., Bogucka, E. P., & Quercia, D. (2025) · Urban Informatics · doi:10.1007/s44212-025-00074-y · Cited by 2 · see citing works
Exploring Accurate and Generic Simile Knowledge from Pre-trained Language Models
Zhou, S., Ma, L., & Shao, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6207-5_22 · Cited by 1 · see citing works
A humble proposal for GeoAI: Epistemology, methodology, and relevance in curb ramp classification
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Mandapati, S. V., Kummetha, V. C., Concas, S., & Staes, L. (2025) · Electronics · doi:10.3390/electronics15010152 · Cited by 1 · see citing works
A White-Box Benchmark for Intrinsic Hallucination Detection in Turkish LLMs
Bayramoglu, B., Ficici, M., Bildirici, F., Kızıltepe, R. S., & Karakuş, M. (2026) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec69317.2026.11418463 · Cited by 1 · see citing works
Truong, V. Q., & Ritchie, M. D. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag256 · Cited by 1 · see citing works
Reranking for a Polish Medical Search Engine
Pokrywka, J., Jassem, K., Wierzchoń, P., Badylak, P., & Kurzyp, G. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f1627 · Cited by 1 · see citing works
Comprehending and Reducing LLM Hallucinations
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Introduction of Data Quality Management
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Alucinaciones de la inteligencia artificial: impacto en tecnología, política y sociedad
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Beyond the Turing Test: A Bergsonian Exploration of Proto-Consciousness in Large Language Models
Thomas, A. (2025) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s2705078524500097 · Cited by 1 · see citing works
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
Daheim, N., Thulke, D., Dugast, C., & Ney, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.98 · Cited by 1 · see citing works
Data Disquiet: Concerns about the Governance of Data for Generative AIPreprint
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Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
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Geo-Seq2seq: Twitter User Geolocation on Noisy Data through Sequence to Sequence Learning
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Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Yang, Y., Huang, H., Liu, Y., & Gao, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.982
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
Jang, Y., Son, S., Lee, J., Son, J., Hur, Y., Lim, J., Moon, H., Yang, K., & Lim, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.295
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LINGUAGEM, INTENCIONALIDADE E O PROBLEMA DA INTELIGÊNCIA ARTIFICIAL
Teles, L. N. (2024) · Kínesis - Revista de Estudos dos Pós-Graduandos em Filosofia · doi:10.36311/1984-8900.2024.v16n40.p310-323
- Authors
- Dziri, N., Milton, S., Yu, M., Zaiane, O., & Reddy, S.
- Affiliations
- University of Alberta (CA)
- McGill University (CA)
- Corresponding author
- Dziri, Nouha
- ORCID
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 5271-5285
- OpenAlex
- W4287854593
- Cited by
- 121
- References
- 30
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 33 · 2023: 42 · 2022: 5 · 2015: 1
- FWCI
- 14.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- Canadian Institute for Advanced Research
- Alberta Machine Intelligence Institute
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- hallucinations, dialogue model, large language models, data bias, conversational AI
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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