  Calibration strengthens the trustworthiness of black-box models by producing
better accurate confidence estimates on given examples. However, little is
known about if model explanations can help confidence calibration. Intuitively,
humans look at important features attributions and decide whether the model is
trustworthy. Similarly, the explanations can tell us when the model may or may
not know. Inspired by this, we propose a method named CME that leverages model
explanations to make the model less confident with non-inductive attributions.
The idea is that when the model is not highly confident, it is difficult to
identify strong indications of any class, and the tokens accordingly do not
have high attribution scores for any class and vice versa. We conduct extensive
experiments on six datasets with two popular pre-trained language models in the
in-domain and out-of-domain settings. The results show that CME improves
calibration performance in all settings. The expected calibration errors are
further reduced when combined with temperature scaling. Our findings highlight
that model explanations can help calibrate posterior estimates.
