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Similarity measures are a vital tool for understanding how language modelsrepresent and process language. Standard representational similarity measuressuch as cosine similarity and Euclidean distance have been successfully used instatic word embedding models to understand how words cluster in semantic space.Recently, these measures have been applied to embeddings from contextualizedmodels such as BERT and GPT-2. In this work, we call into question theinformativity of such measures for contextualized language models. We find thata small number of rogue dimensions, often just 1-3, dominate these measures.Moreover, we find a striking mismatch between the dimensions that dominatesimilarity measures and those which are important to the behavior of the model.We show that simple postprocessing techniques such as standardization are ableto correct for rogue dimensions and reveal underlying representational quality.We argue that accounting for rogue dimensions is essential for anysimilarity-based analysis of contextual language models.

All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality