Calibrating Semantic Uncertainty from Observable Language-Model Probabilities
As generative artificial intelligence enters scientific and professional work, its uncertainty must be defined on the states that matter for inference and decision-making. Language models assign probabilities to words, whereas applications require uncertainty over meaningful states such as diagnoses, hypotheses or operational conditions. We introduce a \emph{semantic map}: a prespecified, testable bridge from probabilities over verbal responses to a posterior over declared finite states. The language distribution remains unrestricted; held-out calibration connects it to a reference posterior. We derive posterior-error bounds and conditions for existence, conditional uniqueness, presentati...
| Authors | Matthew F. Dixon |
|---|---|
| Primary category | stat.ME |
| Published | 20 July 2026 |
| Last updated at source | 4 August 2026 |
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