Calibration of Pre-trained Transformers

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 (Devlin et al., 2019) and RoBERTa For each task, we consider in-domain as well as challenging outof-domain settings, where models face more examples they should be uncertain about. We show that: (1) when used out-of-the-box, pretrained models are calibrated in-domain, and compared to baselines, their calibration error out-of-domain can be as much as 3.5 lower;