  We develop a novel approach for confidently accelerating inference in the
large and expensive multilayer Transformers that are now ubiquitous in natural
language processing (NLP). Amortized or approximate computational methods
increase efficiency, but can come with unpredictable performance costs. In this
work, we present CATs -- Confident Adaptive Transformers -- in which we
simultaneously increase computational efficiency, while guaranteeing a
specifiable degree of consistency with the original model with high confidence.
Our method trains additional prediction heads on top of intermediate layers,
and dynamically decides when to stop allocating computational effort to each
input using a meta consistency classifier. To calibrate our early prediction
stopping rule, we formulate a unique extension of conformal prediction. We
demonstrate the effectiveness of this approach on four classification and
regression tasks.
