  In this paper, we propose Patience-based Early Exit, a straightforward yet
effective inference method that can be used as a plug-and-play technique to
simultaneously improve the efficiency and robustness of a pretrained language
model (PLM). To achieve this, our approach couples an internal-classifier with
each layer of a PLM and dynamically stops inference when the intermediate
predictions of the internal classifiers remain unchanged for a pre-defined
number of steps. Our approach improves inference efficiency as it allows the
model to make a prediction with fewer layers. Meanwhile, experimental results
with an ALBERT model show that our method can improve the accuracy and
robustness of the model by preventing it from overthinking and exploiting
multiple classifiers for prediction, yielding a better accuracy-speed trade-off
compared to existing early exit methods.
