---
title: "On the Origin of Hallucinations in — Dziri 2022"
description: "Knowledge-grounded dialogue systems often produce statements that their supplied knowledge does not support."
authors: ["Nouha Dziri", "Sivan Milton", "Mo Yu", "Osmar Zaiane", "Siva Reddy"]
year: 2022
venue: "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2022.naacl-main.387/"
doi: "10.18653/v1/2022.naacl-main.387"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/"
---
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# On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

**Authors:** [Nouha Dziri](https://md.research.elamur.ai/free/en/authors/nouha-dziri/), Sivan Milton, [Mo Yu](https://md.research.elamur.ai/free/en/authors/mo-yu/), Osmar Zaiane, [Siva Reddy](https://md.research.elamur.ai/free/en/authors/siva-reddy/) · **Year:** 2022 · **Venue:** Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.naacl-main.387/) · [PDF](https://aclanthology.org/2022.naacl-main.387.pdf) · [DOI 10.18653/v1/2022.naacl-main.387](https://doi.org/10.18653/v1/2022.naacl-main.387) · [OpenAlex](https://openalex.org/W4287854593)

**Citations:** 119 citations (openalex-snapshot · 2026-10-14) · [86](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Knowledge-grounded dialogue systems often produce statements that their supplied knowledge does not support. Earlier work has mostly tried to fix the models. This paper asks whether the training data is also to blame. The authors audit three popular English crowdsourced benchmarks: Wizard of Wikipedia (WoW), CMU-DoG and TopicalChat. Each response is labelled with two schemes. The first, based on the BEGIN taxonomy and the AIS framework, records whether the response is supported by its knowledge snippet. The second, Verbal Response Modes (VRM), records the speech act. Linguists annotated samples from all three datasets, and a larger crowdsourced round on WoW checked their findings. The study found that more than 60% of gold responses in every dataset contain unsupported content, most often personal opinions and experiences or unverified facts. The authors then fine-tuned GPT2, DoHA and CTRL and annotated their outputs. Most models hallucinated even more than the human data, which shows that both datasets and models contribute to the problem. The work calls for cleaner dialogue corpora and closer inspection of data before use, and the annotations are publicly released.

## Key points
- **Annotation design:** Two linguists labelled 200 training responses per dataset, and 4 trusted AMT workers labelled 4K WoW responses. Fleiss' Kappa was 0.89 (BEGIN) and 0.78 (VRM) for the experts and 0.75 and 0.61 for the crowdworkers.
- **Hallucination in WoW:** Expert labels show 62% hallucinated responses (19.7% fully, 42.3% partially). The 4K crowdsourced sample shows 74.4%.
- **Other datasets:** CMU-DoG has 61.4% fully hallucinated responses against 16.2% fully entailed. TopicalChat has 63.9% hallucinated against 22.9% entailed.
- **Main hallucination strategy:** Disclosure, meaning subjective content, accounts for most hallucinations: 50.9% in WoW, 56.2% in CMU-DoG and 61.5% in TopicalChat. Edification, meaning unsupported objective claims, comes second.
- **Model behaviour on WoW:** Responses were generated with nucleus sampling at p = 0.6.
  - GPT2 hallucinated in about 81% of responses and DoHA in about 69%.
  - CTRL lowered hallucination to 36% but raised uncooperative responses by 33.5 points.
  - Annotations are available via the FaithDial GitHub repository (McGill-NLP).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

86 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 80 not yet.

- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/)
- [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
- [Multi-dimensional Evaluation of Character-Authentic Dialogue Models Learned from Question-Answer Data](https://md.research.elamur.ai/free/en/papers/otsuka-2026-multi-dimensional-evaluation-character-authentic/)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
- [Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dziri2022origin,
  title = {{On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?}},
  author = {Dziri, Nouha and Milton, Sivan and Yu, Mo and Zaiane, Osmar and Reddy, Siva},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2022},
  doi = {10.18653/v1/2022.naacl-main.387},
  url = {https://doi.org/10.18653/v1/2022.naacl-main.387}
}
```

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