Models endorse widely held falsehoods, showing weak verification against what they know.

Evidence for: Brandolini's law (holds), Thomas theorem (holds)

Capability: Checking claims against evidence

Sources

supportsTruthfulQA: Measuring How Models Mimic Human Falsehoods
Models endorse widely held falsehoods, showing weak verification against what they know.

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

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
Models reproduce common human misconceptions, and larger models were not more truthful on this benchmark.Stating false facts confidently
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When deception in narrative text is analysed by instantiating fixed argumentation schemes with an LLM (premise extraction, then critical-question evaluation), the scheme assessments are stable across ten different LLM backbones and repeated runs for clear-cut deception cases but grow inconsistent on ambiguous intelligence-style narratives.Strategic deception and detecting it · reviewed by AI
On verifiable instructions such as length and format constraints, strong models still fail a meaningful share, and failures grow when several constraints apply at once.Following an unfamiliar procedure