Hallucination Types in Language Models: A Complete Guide › Summary
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Hallucination in language models describes generated content that is unfaithful to a source, factually incorrect, or entirely fabricated. The key type distinctions are:
- Intrinsic hallucination: directly contradicts the provided source document, detectable by comparing output to input using NLI
- Extrinsic hallucination: adds information not in the source, not necessarily factually wrong but ungrounded and unverifiable
- Factual errors: wrong claims about world knowledge (wrong entity facts, numerical errors, outdated information, relationship errors)
- Fabrication: inventing entities, citations, statistics, or events that do not exist
- Inconsistency: self-contradictions within a response (intra-document) or across conversation turns (cross-turn)
The intrinsic/extrinsic distinction is most useful in closed-domain tasks like summarization and RAG-based QA, where NLI-based evaluation can automatically detect contradictions between output and source. For open-domain generation, factual verification against external knowledge bases is required. Inconsistency is unique in that it requires no external reference: the output contradicts itself, and this is detectable from the output alone.
NLI-based scoring works well for intrinsic hallucinations but scores extrinsic ones as "neutral," motivating hybrid evaluation approaches that combine NLI with external knowledge lookup. Self-consistency checking complements NLI by detecting uncertainty and potential inconsistency without any external knowledge. The next chapter, Hallucination Detection, covers these methods in depth, including entailment models, self-consistency checking, and knowledge base verification.
The taxonomy is not just academic: it dictates which evaluation tools apply, which mitigation strategies are appropriate, and what a deployable reliability guarantee looks like for a given task. Understanding hallucination types is the prerequisite for building systems that fail gracefully and communicate their limitations clearly.