Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty

Uncertainty Quantification is a large and growing subfield of large language model behavioral analysis. Primarily to recognize and combat hallucination, the field has largely focused on measuring and improving calibration, the accuracy of uncertainty judgments to task efficacy. In this work, we investigate the relatively underexplored question of how similar large language model uncertainty is to human uncertainty. We investigate the presence and strength of human-similar uncertainty signals, de

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ethics.ai (29 May 2026), “Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty,” evidence record 3468, https://ethics.ai/record/3468 (originally published by arXiv).

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