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We show that a GPT-3 model can learn to express uncertainty about its ownanswers in natural language -- without use of model logits. When given aquestion, the model generates both an answer and a level of confidence (e.g."90% confidence" or "high confidence"). These levels map to probabilities thatare well calibrated. The model also remains moderately calibrated underdistribution shift, and is sensitive to uncertainty in its own answers, ratherthan imitating human examples. To our knowledge, this is the first time a modelhas been shown to express calibrated uncertainty about its own answers innatural language. For testing calibration, we introduce the CalibratedMathsuite of tasks. We compare the calibration of uncertainty expressed in words("verbalized probability") to uncertainty extracted from model logits. Bothkinds of uncertainty are capable of generalizing calibration under distributionshift. We also provide evidence that GPT-3's ability to generalize calibrationdepends on pre-trained latent representations that correlate with epistemicuncertainty over its answers.

Teaching Models to Express Their Uncertainty in Words