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This is a Quantitative Study study.

October 13, 2025Open Access

When an LLM is apprehensive about its answers -- and when its uncertainty is justified

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Token-wise entropy serves as an effective predictor of model errors in knowledge-dependent domains.
Experiments involving three LLMs of varying sizes showed variance in accuracy across different domains, specifically a ROC AUC of 0.73 for biology.
Model-as-judge performs similarly to random predictors, highlighting the need for refinement in this uncertainty estimation method.
To achieve fair LLM assessments, the data uncertainty related to entropy should integrate reasoning requirements across subdomains.

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This research investigates uncertainty estimation approaches in large language models, revealing implications for performance evaluation.

Sychev et al. (2025) studied this question.

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