  We present the LM Transparency Tool (LM-TT), an open-source interactive
toolkit for analyzing the internal workings of Transformer-based language
models. Differently from previously existing tools that focus on isolated parts
of the decision-making process, our framework is designed to make the entire
prediction process transparent, and allows tracing back model behavior from the
top-layer representation to very fine-grained parts of the model. Specifically,
it (1) shows the important part of the whole input-to-output information flow,
(2) allows attributing any changes done by a model block to individual
attention heads and feed-forward neurons, (3) allows interpreting the functions
of those heads or neurons. A crucial part of this pipeline is showing the
importance of specific model components at each step. As a result, we are able
to look at the roles of model components only in cases where they are important
for a prediction. Since knowing which components should be inspected is key for
analyzing large models where the number of these components is extremely high,
we believe our tool will greatly support the interpretability community both in
research settings and in practical applications.
