[2605.02915] When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signal
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Computer Science > Computation and Language
arXiv:2605.02915 (cs)
[Submitted on 8 Apr 2026]
Title:When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signal
Authors:Aditya Ajay Phalod
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Abstract: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. The results are sharply task- and model-dependent. On ARC-Challenge, self-verification substantially improves over LL-AVG for Phi-2 and the Qwen models, with the largest gains appearing in Qwen-7B. On TruthfulQA-MC, however, the signal is less reliable: smaller models can become prompt-sensitive, DeepSeek-R1-Distill-8B degrades relative to LL-AVG, and LL-SUM often remains the stronger practical baseline. We therefore do not treat self-verification as a general-purpose uncertainty estimator. In this setting, it is better understood as a conditional confidence signal whose value depends on task type, model family, prompt formulation, and, crucially, the baseline it must beat.
Comments: 30 pages, 13 figures, 8 tables. Code available on GitHub
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.02915 [cs.CL]
(or arXiv:2605.02915v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.02915
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arXiv-issued DOI via DataCite
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From: Aditya Phalod [view email] [v1] Wed, 8 Apr 2026 20:15:33 UTC (1,346 KB)
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