Hallucination Types in Language Models: A Complete Guide › Fabrication

Fabrication is the most dramatic form of hallucination: the model does not just get a fact wrong, it invents something that does not exist at all.

Fabrication is qualitatively different from factual errors in ways that matter for both evaluation and mitigation. A factual error involves a real entity with a wrong attribute; a fabrication invents the entity itself, or creates an entirely fictional event, citation, or statistic. The distinction is important because detecting a factual error requires knowing the correct value, while detecting a fabrication requires knowing that the thing being described does not exist. These are different computational problems, and they motivate different verification strategies.

One of the most well-documented hallucination failure modes is citation fabrication. When asked to provide references or sources, language models frequently generate plausible-looking but entirely fictional academic citations:

• Papers with real author names but invented titles, journals, and years
• DOI numbers that follow the correct format but point to nothing
• URLs that look legitimate but lead to nonexistent pages
• Quotes attributed to real people that they never said

This is particularly insidious in academic and professional contexts. A fabricated citation has all the surface features of a real one: correct author name formatting, journal name capitalization, proper DOI structure. A busy reader might not think to verify it. A student who includes the citation in their own work passes on the fabrication. A professional who cites it in a report creates a paper trail built on nothing.

The problem is systematic, not occasional. Studies have found that language models can hallucinate citations at rates exceeding 50% when asked to provide references for specific claims. This figure is sobering: when you ask a language model to "cite three papers supporting this claim," there is a better-than-even chance that at least one of those citations does not exist. The model has learned what a citation looks like from its training data and generates convincing-looking references without any mechanism to verify that they exist.

The reason citation fabrication is so common relates to the structure of the task. Providing a citation involves producing a specific, formatted string: an author name in a particular style, a title that sounds plausible for the claimed topic, a journal that publishes in the relevant field, and a year that is plausible given the timeline of the field. The model can produce each of these components correctly without the components combining into a citation that points to a real paper. It is generating a plausible structure, not a verified reference.

Beyond citations, models sometimes invent entire entities:

• Fake companies: "Founded in 2015, NovaTech Analytics specializes in supply chain optimization..." (no such company exists)
• Fake people: Attributing expertise or quotes to professionals who do not exist
• Fake events: Describing conferences, studies, or announcements that never happened
• Fake statistics: Citing survey results or research findings with completely fabricated numbers

These fabrications are particularly dangerous when they are embedded in otherwise accurate content. A response that is 95% correct with a single fabricated statistic is more dangerous than a response that is obviously wrong throughout, because the fabrication inherits credibility from the surrounding accurate content. A user reading a well-organized, clearly written response is unlikely to scrutinize any single claim, especially when all the surrounding claims appear to be accurate. The fabrication rides along on the credibility of the correct content.

Entity fabrication is also harder to catch than factual errors precisely because the fabricated entity does not exist. A fact-check tool that looks up "NovaTech Analytics" will find no information, which could mean the entity is obscure rather than fabricated. The absence of evidence is not, in itself, conclusive evidence of fabrication, which makes automated detection of entity fabrication a significantly harder problem than detecting wrong values for known entities.

The common thread across fabrication types is plausibility. Models do not generate random nonsense. They generate content that:

• Fits the statistical patterns of the surrounding text
• Matches the expected format and register for the context
• Uses real names, places, and concepts as anchors
• Fills in gaps with the most likely-seeming content

This is, in a sense, what language models are optimized to do: generate the most likely continuation. When that continuation happens to be a fact the model has correctly stored, the result is accurate. When it is a pattern that gets filled with plausible-sounding but incorrect content, the result is a convincing hallucination. The model has no internal signal distinguishing these two cases.

The plausibility of fabricated content explains why hallucination is such a difficult problem to solve at the level of model training. A model trained to avoid producing implausible text will, if anything, become better at producing plausible fabrications, not worse. The goal of fluency and the goal of factual accuracy are not aligned, and training improvements that improve one can worsen the other. This fundamental tension motivates the external verification approaches we will examine in the Hallucination Detection chapter.

There is also a systematic pattern to what gets fabricated. Models are more likely to fabricate in areas where training data was sparse, where the topic requires highly specific factual knowledge, or where the model is being asked to produce structured outputs (tables, lists, citations) that require many specific values to be correct simultaneously. Understanding these patterns helps system designers anticipate where fabrication risks are highest and apply targeted mitigation strategies.