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Transformers, currently the state-of-the-art in natural languageunderstanding (NLU) tasks, are prone to generate uncalibrated predictions orextreme probabilities, making the process of taking different decisions basedon their output relatively difficult. In this paper we propose to build severalinductive Venn--ABERS predictors (IVAP), which are guaranteed to be wellcalibrated under minimal assumptions, based on a selection of pre-trainedtransformers. We test their performance over a set of diverse NLU tasks andshow that they are capable of producing well-calibrated probabilisticpredictions that are uniformly spread over the [0,1] interval -- all whileretaining the original model's predictive accuracy.

Calibration of Natural Language Understanding Models with Venn--ABERS Predictors