Hallucination Detection in LLMs
Hallucination detection is the set of techniques for flagging when a language model states something fluent but false or unsupported, using signals like self-consistency, model uncertainty, and grounding against retrieved evidence to catch fabrications before…
Language models generate plausible text token by token without an inherent notion of truth, so they can produce confident fabrications. Hallucination detection tries to identify these unsupported claims rather than prevent them entirely. The mental model: a hallucination is an output not grounded in any reliable source or in the model's stable internal knowledge.
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