Also Small Models Can Reasonably Self-Evaluate Their Confidence

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.

Machine Learning (cs.LG)

arXiv:2609.39478 [cs.LG]

(or arXiv:2609.39478v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.39478 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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