Also Small Models Can Reasonably Self-Evaluate Their Confidence

Idil Kapikiran; Thomas Decker; Thomas Runkler

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arXiv · 2609.39478

Also Small Models Can Reasonably Self-Evaluate Their Confidence

Idil Kapikiran·Thomas Decker·Thomas Runkler

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Abstract

This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answering tasks spanning general to specialized knowledge domains. Using various self-evaluation methods where models judge their own predictions, we examine how model scale and domain specificity affect the quality of self-assessed confidence signals. Our results reveal that while accuracy predictably declines with smaller models and more specialized domains, the reliability of self-evaluated confidence remains largely stable across both dimensions. This independence means the most capable model is not necessarily the best at self-assessing prediction reliability. These findings suggest that smaller models can achieve reasonable self-assessed confidence despite lower accuracy, making them viable for resource-constrained deployments.

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BibTeX RIS

Idil Kapikiran, Thomas Decker, Thomas Runkler. 2026-09-30. Also Small Models Can Reasonably Self-Evaluate Their Confidence. https://arxiv.org/abs/2609.39478

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