  Recent work has compared neural network representations via similarity-based
analyses to improve model interpretation. The quality of a similarity measure
is typically evaluated by its success in assigning a high score to
representations that are expected to be matched. However, existing similarity
measures perform mediocrely on standard benchmarks. In this work, we develop a
new similarity measure, dubbed ContraSim, based on contrastive learning. In
contrast to common closed-form similarity measures, ContraSim learns a
parameterized measure by using both similar and dissimilar examples. We perform
an extensive experimental evaluation of our method, with both language and
vision models, on the standard layer prediction benchmark and two new
benchmarks that we introduce: the multilingual benchmark and the image-caption
benchmark. In all cases, ContraSim achieves much higher accuracy than previous
similarity measures, even when presented with challenging examples. Finally,
ContraSim is more suitable for the analysis of neural networks, revealing new
insights not captured by previous measures.
