Examples
Worked examples
- Is an instance
An LLM citing a paper with a real-looking DOI that resolves to nothing
- Is an instance
A chatbot attributing a plausible but invented quote to a named scholar
Counter-examples
Looks similar, but isn't
- Not an instance
An LLM giving a wrong answer because the user's prompt was ambiguous (this is misunderstanding, not hallucination in the technical sense)
Editorial commentary
Hallucinations are a structural property of probabilistic language models, not a bug to be patched. The disclosure-relevant implication is that any AI-generated factual content (citations, numbers, attributions) used in a scholarly work must be independently verified by the human author, who remains responsible for accuracy.
References
- Ji et al. 2023 ‘Survey of Hallucination in NLG’ ACM Computing Surveys
- Bender et al. 2021 ‘Stochastic Parrots’ FAccT
Also known as
Confabulation · AI fabrication
Machine-readable encodings
Use in your systems
xml
<role vocab="credit"
vocab-identifier="https://casrai.org/dictionary/"
vocab-term="Hallucination"
vocab-term-identifier="https://casrai.org/dictionary/term/hallucination" />json
{
"@context": "https://schema.org",
"@type": "DefinedTerm",
"name": "Hallucination",
"identifier": "https://casrai.org/dictionary/term/hallucination",
"description": "An output from a generative AI system that is presented confidently and fluently but is factually incorrect, fabricated, or unsupported by the input data or any verifiable source — including invented citations, non-existent authors, false statistics, and incorrect quotations.",
"inDefinedTermSet": "https://casrai.org/dictionary/domain/generative-ai-use-and-disclosure/",
"url": "https://casrai.org/dictionary/term/hallucination",
"sameAs": [
"Confabulation",
"AI fabrication"
],
"license": "https://creativecommons.org/licenses/by/4.0/"
}
