What Cosine Similarity of Label Representations Can and Cannot Tell us
Cosine similarity is often used to measure the similarity of vector representations of neural network models. However, the cosine similarity of representations is not guaranteed to tell us anything about model probabilities. In this paper we show that for a softmax classifier, be it an image classifier or an autoregressive language model, the cosine similarity between label representations (called unembeddings in the paper) does not give any information on the probabilities assigned by the model. Specifically, we prove that given two unembeddings, it is possible to create another model which assigns the same probabilities for all inputs, but where the cosine similarity between the represe...
| Authors | Beatrix M. G. Nielsen, Andreas Grivas |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 1 June 2026 |
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