The Computational Basis of Confidence in Large Language Models
Reliable confidence -- the probability that a model's own answer is correct -- is essential for the trustworthy deployment of language models. Existing work has largely evaluated confidence by how well it predicts correctness and whether it is calibrated, leaving open a more fundamental question: what does the confidence signal itself represent? Answer logits may reflect a latent decision variable sufficient to compute normative confidence, or instead a heuristic preference signal that combines the available evidence in a non-Bayesian manner. We address this using statistical decision confidence (SDC), a normative framework from computational neuroscience. Treating the answer-logit differ...
| Authors | Dharshan Kumaran, Viorica Patraucean, Maks Ovsjanikov et al. |
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| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 24 July 2026 |
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