Confidence Calibration in Large Language Models
We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the current crop of LLMs are, like people, too sure they are right: confidence exceeds accuracy, on average. Importantly, however, this tendency is moderated by a powerful hard-easy effect, wherein overconfidence is greatest on difficult tests; by contrast, easy tests actually show substantial underconfidence. We develop LifeEval, a test for evaluating model calibration across levels of difficulty.
| Authors | Noam Michael, Daniel BenShushan, Jacob Bien et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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