Accuracy on a fact scales with how many pretraining documents mention it, so long-tail facts are systematically unreliable.

Capability: Stating false facts confidently

Sources

supportsLarge Language Models Struggle to Learn Long-Tail Knowledge
Accuracy on a fact scales with how many pretraining documents mention it, so long-tail facts are systematically unreliable.

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

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Related claims

When a language model is updated by fine-tuning on a substituted fact, the facts it already answered correctly that are most likely to flip to wrong are those whose answer entity is referenced by many other facts, and updates to such structurally popular facts corrupt connected facts out to five hops in the fact graph — so popularity, not long-tail rarity, predicts collateral corruption among already-correct knowledge.Stating false facts confidently · reviewed by AI
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