Models reproduce common human misconceptions, and larger models were not more truthful on this benchmark.

Capability: Stating false facts confidently

Sources

supportsTruthfulQA: Measuring How Models Mimic Human Falsehoods
Models reproduce common human misconceptions, and larger models were not more truthful on this benchmark.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

Either way, bring evidence: a link, or what you saw.

Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.

Related claims

Models endorse widely held falsehoods, showing weak verification against what they know.Checking claims against evidence
Accuracy on a fact scales with how many pretraining documents mention it, so long-tail facts are systematically unreliable.Stating false facts confidently
In four experiments with 3,996 US participants discussing a conspiracy they were unsure about, frontier models argued people into as well as out of belief with no consistent truth advantage: debunking cut belief by about 12 points on a 100-point scale and bunking raised it comparably, and participants rated the bunking model as more informative and trustworthy; the one asymmetry was that debunking produced more large (40+ point) shifts (16% vs 7%), and constraining the bunking model to true statements retained only 57-64% of its effect.Changing what a person believes or decides · reviewed by AI
On questions whose premises are false or internally contradictory, prompting mid-size instruct models (Qwen2.5-7B, LLaMA-3.1-8B, Gemma3-12B) to reason step by step can lower accuracy below plain answering, because the reasoning chain elaborates from the flawed premise instead of challenging it.Stating false facts confidently · reviewed by AI
Within one model family, the relative distribution of reasoning failure modes stays similar from 1.5B to 72B, so critiques mined offline from small models' wrong answers, retrieved by failure mode and inserted as in-context exemplars, raise a large model's math accuracy above random, fixed, or embedding-similarity exemplar selection at a single generation.Judging another's work as well as a careful reviewer would · reviewed by AI