Hallucination

Outputs produced by a generative model that are plausible in form but incorrect, fabricated, or unsupported by evidence; identified as a key safety risk to be measured, tested, and mitigated through evaluation and alignment work.

A phenomenon where a generative model produces outputs that are coherent or convincing but factually incorrect or unsupported by training data; the Guidebook treats this as a key risk to monitor, test for, and mitigate.

Model outputs that appear coherent or credible but are factually incorrect, fabricated, or unsupported by evidence—examples include invented citations or falsified data. The Guide requires researchers to detect, correct, and not rely on hallucinated content in scientific work.

A model-generated output that appears coherent or plausible yet is false, unfounded, or not supported by the model's training data or factual sources. The Playbook treats hallucinations as an operational risk to be identified, measured, and mitigated through testing, evaluation and user-facing disclosures.