Hallucination Types in Language Models: A Complete Guide › Closed-Domain vs Open-Domain Hallucinations

The hallucination taxonomy also maps onto the task domain, which shapes how you evaluate and mitigate it.

Closed-domain tasks provide a source document and ask the model to work within it: document summarization, retrieval-augmented QA, document classification with rationale. In these settings, hallucinations are primarily evaluated against the source document, using the intrinsic/extrinsic framework. The key question is: did the model stay within the bounds of what the document says?

Open-domain tasks involve general knowledge generation without a specific source: answering factual questions, writing explanations, giving recommendations. Here there is no source document to check against, so hallucinations are evaluated against world knowledge. Factual errors and fabrication are the primary concern.

This distinction has direct implications for evaluation strategy. Closed-domain hallucinations can often be caught with automated NLI-based metrics (comparing output to source). Open-domain hallucinations require either human judgment or access to external knowledge bases for automated verification.

In practice, many real-world systems occupy a middle ground. Retrieval-augmented generation (RAG) systems fetch relevant documents at query time and ask the model to answer using that retrieved context, making the task superficially closed-domain. But the model still relies on its parametric knowledge for synthesis, for interpreting what the retrieved documents mean, and for filling in gaps when the retrieved context is incomplete. This creates a hybrid evaluation challenge: you need to catch intrinsic hallucinations (the model contradicting retrieved context), extrinsic hallucinations (the model adding information not in retrieved context), and factual errors from parametric knowledge (the model reasoning incorrectly about the retrieved content). The Hallucination Mitigation chapter explores how RAG system design choices affect each of these hallucination types and what mitigation strategies work best in each case.

Rather than a binary distinction between closed-domain and open-domain tasks, it is more accurate to describe a grounding spectrum. At one end, highly constrained closed-domain tasks (extractive summarization, where the model is explicitly restricted to copying information from the source) have near-zero opportunity for most hallucination types. Moving along the spectrum, abstractive summarization introduces some freedom that allows extrinsic hallucinations. RAG-based generation adds even more freedom, since the model must synthesize across multiple retrieved documents and fill gaps. Open-domain generation sits at the far end, where any factual claim can potentially be a hallucination.

The practical implication is that you should calibrate your hallucination evaluation and mitigation strategy to where your task sits on this spectrum. A system at the extractive end needs minimal hallucination mitigation but careful NLI-based faithfulness checking. A system at the open-domain end needs reliable fact-checking pipelines, uncertainty quantification, and user-facing caveats about the limits of model knowledge.