  While Transformer-based language models are generally very robust to pruning,
there is the recently discovered outlier phenomenon: disabling only 48 out of
110M parameters in BERT-base drops its performance by nearly 30% on MNLI. We
replicate the original evidence for the outlier phenomenon and we link it to
the geometry of the embedding space. We find that in both BERT and RoBERTa the
magnitude of hidden state coefficients corresponding to outlier dimensions
correlates with the frequency of encoded tokens in pre-training data, and it
also contributes to the "vertical" self-attention pattern enabling the model to
focus on the special tokens. This explains the drop in performance from
disabling the outliers, and it suggests that to decrease anisotropicity in
future models we need pre-training schemas that would better take into account
the skewed token distributions.
