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Can we preserve the accuracy of neural models while also providing faithfulexplanations? We present wrapper boxes, a general approach to generatefaithful, example-based explanations for model predictions while maintainingpredictive performance. After training a neural model as usual, its learnedfeature representation is input to a classic, interpretable model to performthe actual prediction. This simple strategy is surprisingly effective, withresults largely comparable to those of the original neural model, as shownacross three large pre-trained language models, two datasets of varying scale,four classic models, and four evaluation metrics. Moreover, because theseclassic models are interpretable by design, the subset of training examplesthat determine classic model predictions can be shown directly to users.

Interpretable by Design: Wrapper Boxes Combine Neural Performance with Faithful Explanations