  The question of what kinds of linguistic information are encoded in different
layers of Transformer-based language models is of considerable interest for the
NLP community. Existing work, however, has overwhelmingly focused on word-level
representations and encoder-only language models with the masked-token training
objective. In this paper, we present experiments with semantic structural
probing, a method for studying sentence-level representations via finding a
subspace of the embedding space that provides suitable task-specific pairwise
distances between data-points. We apply our method to language models from
different families (encoder-only, decoder-only, encoder-decoder) and of
different sizes in the context of two tasks, semantic textual similarity and
natural-language inference. We find that model families differ substantially in
their performance and layer dynamics, but that the results are largely
model-size invariant.
