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We design probes trained on the internal representations of a transformerlanguage model that are predictive of its hallucinatory behavior on in-contextgeneration tasks. To facilitate this detection, we create a span-annotateddataset of organic and synthetic hallucinations over several tasks. We findthat probes trained on the force-decoded states of synthetic hallucinations aregenerally ecologically invalid in organic hallucination detection. Furthermore,hidden state information about hallucination appears to be task anddistribution-dependent. Intrinsic and extrinsic hallucination saliency variesacross layers, hidden state types, and tasks; notably, extrinsic hallucinationstend to be more salient in a transformer's internal representations.Outperforming multiple contemporary baselines, we show that probing is afeasible and efficient alternative to language model hallucination evaluationwhen model states are available.

Do Androids Know They're Only Dreaming of Electric Sheep?