  We analyze the operation of transformer language adapters, which are small
modules trained on top of a frozen language model to adapt its predictions to
new target languages. We show that adapted predictions mostly evolve in the
source language the model was trained on, while the target language becomes
pronounced only in the very last layers of the model. Moreover, the adaptation
process is gradual and distributed across layers, where it is possible to skip
small groups of adapters without decreasing adaptation performance. Last, we
show that adapters operate on top of the model's frozen representation space
while largely preserving its structure, rather than on an 'isolated' subspace.
Our findings provide a deeper view into the adaptation process of language
models to new languages, showcasing the constraints imposed on it by the
underlying model and introduces practical implications to enhance its
efficiency.
