  Pre-trained Transformers are now ubiquitous in natural language processing,
but despite their high end-task performance, little is known empirically about
whether they are calibrated. Specifically, do these models' posterior
probabilities provide an accurate empirical measure of how likely the model is
to be correct on a given example? We focus on BERT and RoBERTa in this work,
and analyze their calibration across three tasks: natural language inference,
paraphrase detection, and commonsense reasoning. For each task, we consider
in-domain as well as challenging out-of-domain settings, where models face more
examples they should be uncertain about. We show that: (1) when used
out-of-the-box, pre-trained models are calibrated in-domain, and compared to
baselines, their calibration error out-of-domain can be as much as 3.5x lower;
(2) temperature scaling is effective at further reducing calibration error
in-domain, and using label smoothing to deliberately increase empirical
uncertainty helps calibrate posteriors out-of-domain.
