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

通过标准化技术等方法探讨相似度测量在 BERT 和 GPT-2 等上下文鉴别模型中的代表性和准确性。我们发现一些不代表整体性质的维度会干扰这些测量，并且这些测量值并不一定代表模型的真实行为。因此，我们认为分析这些模型中基于相似性的操作需要解决这些问题。