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Extrinsic Hallucinations in LLMs

Named practitioner synthesis

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Canonical URL: https://lilianweng.github.io/posts/2024-07-07-hallucination/
Topic: Practitioner Notes
First seen: 2026-07-16 19:08:00
Last seen: 2026-07-16 19:08:00

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Blog / Newsletter2026-07-16 19:07:22
Extrinsic Hallucinations in LLMs
<p>Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and <strong>not grounded</strong> by either the provided context or world knowledge.</p> <p>There are two types of hallucination:</p> <ol> <li>In-context hallucination: The model output should be consistent with the source content in context.</li> <li>Extrinsic hallucination: The model output should be grounded by the pre-training dataset. However, given the size of the pre-training dataset, it is too expensive to retrieve and identify conflicts per generation. If we consider the pre-training data corpus as a proxy for world knowledge, we essentially try to ensure the model output is factual and verifiable by external world knowledge. Equally im