  In this paper, we study the computation of how much an input token in a
Transformer model influences its prediction. We formalize a method to construct
a flow network out of the attention values of encoder-only Transformer models
and extend it to general Transformer architectures including an auto-regressive
decoder. We show that running a maxflow algorithm on the flow network
construction yields Shapley values, which determine the impact of a player in
cooperative game theory. By interpreting the input tokens in the flow network
as players, we can compute their influence on the total attention flow leading
to the decoder's decision. Additionally, we provide a library that computes and
visualizes the attention flow of arbitrary Transformer models. We show the
usefulness of our implementation on various models trained on natural language
processing and reasoning tasks.
