  We describe an "interpretability illusion" that arises when analyzing the
BERT model. Activations of individual neurons in the network may spuriously
appear to encode a single, simple concept, when in fact they are encoding
something far more complex. The same effect holds for linear combinations of
activations. We trace the source of this illusion to geometric properties of
BERT's embedding space as well as the fact that common text corpora represent
only narrow slices of possible English sentences. We provide a taxonomy of
model-learned concepts and discuss methodological implications for
interpretability research, especially the importance of testing hypotheses on
multiple data sets.
