Attention Meets Post-hoc Interpretability: A Mathematical Perspective
Gianluigi Lopardo Frederic Precioso Damien Garreau
Attention-based architectures, in particular transformers, are at the heart
of a technological revolution. Interestingly, in addition to helping obtain
state-of-the-art results on a wide range of applications, the attention
mechanism intrinsically provides meaningful insights on the internal behavior
of the model. Can these insights be used as explanations? Debate rages on. In
this paper, we mathematically study a simple attention-based architecture and
pinpoint the differences between post-hoc and attention-based explanations. We
show that they provide quite different results, and that, despite their
limitations, post-hoc methods are capable of capturing more useful insights
than merely examining the attention weights.