Hallucination (artificial intelligence)

In artificial intelligence (AI), a hallucination is generated content that is false, unsupported, or inconsistent with the information on which the output is supposed to be based. The term is especially associated with large language models (LLMs), which can generate fluent and plausible statements, quotations, citations, or explanations that are not factually correct or are not supported by the supplied source material.

There is no single universally accepted definition of hallucination. Research commonly distinguishes factuality, concerning correspondence with independently verifiable facts, from faithfulness, concerning consistency with a supplied source, context, or instruction. Thus, an output can be factually correct but unsupported by the source it purports to summarize, or faithful to an inaccurate source while remaining false in the external world.

Hallucinations are a reliability problem because incorrect material may be expressed in the same confident and fluent style as correct material. Documented examples include fabricated academic references, nonexistent judicial decisions, inaccurate summaries, invented biographical claims, and incorrect information supplied by customer-service chatbots. The frequency of such errors varies with the model, task, prompting method, available context, sampling procedure, and definition used, so reported hallucination rates are not directly comparable across all systems and benchmarks.