  Can we preserve the accuracy of neural models while also providing faithful
explanations? We present wrapper boxes, a general approach to generate
faithful, example-based explanations for model predictions while maintaining
predictive performance. After training a neural model as usual, its learned
feature representation is input to a classic, interpretable model to perform
the actual prediction. This simple strategy is surprisingly effective, with
results largely comparable to those of the original neural model, as shown
across three large pre-trained language models, two datasets of varying scale,
four classic models, and four evaluation metrics. Moreover, because these
classic models are interpretable by design, the subset of training examples
that determine classic model predictions can be shown directly to users.
