  Understanding how Transformer-based Language Models (LMs) learn and recall
information is a key goal of the deep learning community. Recent
interpretability methods project weights and hidden states obtained from the
forward pass to the models' vocabularies, helping to uncover how information
flows within LMs. In this work, we extend this methodology to LMs' backward
pass and gradients. We first prove that a gradient matrix can be cast as a
low-rank linear combination of its forward and backward passes' inputs. We then
develop methods to project these gradients into vocabulary items and explore
the mechanics of how new information is stored in the LMs' neurons.
