AI RESEARCH
When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signal
arXiv CS.LG
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ArXi:2605.02915v1 Announce Type: cross Same-model self-verification, prompting a model to audit its own predicted answer, is a plausible confidence signal for selective prediction, but its practical value remains unclear once strong likelihood-based baselines are taken seriously. We evaluate self-verification against two such baselines, LL-AVG and LL-SUM, on ARC-Challenge and TruthfulQA-MC across multiple model families, scales, and prompt variants. We measure not only correctness ranking, but also abstention quality through AURC and operating-point analyses.