  Predictive uncertainty estimation of pre-trained language models is an
important measure of how likely people can trust their predictions. However,
little is known about what makes a model prediction uncertain. Explaining
predictive uncertainty is an important complement to explaining prediction
labels in helping users understand model decision making and gaining their
trust on model predictions, while has been largely ignored in prior works. In
this work, we propose to explain the predictive uncertainty of pre-trained
language models by extracting uncertain words from existing model explanations.
We find the uncertain words are those identified as making negative
contributions to prediction labels, while actually explaining the predictive
uncertainty. Experiments show that uncertainty explanations are indispensable
to explaining models and helping humans understand model prediction behavior.
