arXiv:2602.19393v2 Announce Type: replace Abstract: Steck, Ekanadham, and Kallus [arXiv:2403.05440] demonstrate that cosine similarity of learned embeddings from matrix factorization models can be rendered arbitrary by a diagonal ``gauge'' matrix $D$. Their result is correct and important for practitioners who compute cosine similarity on embeddings trained with dot-product objectives. However, we argue that their conclusion, cautioning against cosine similarity in general, conflates the pathology of an incompatible training objective with the geometric validity of cosine distance on the unit
This paper addresses foundational methodological issues in AI/ML, specifically concerning the robust use of cosine similarity for embedding analysis, following previous research highlighting its vulnerabilities.
For practitioners and researchers, this clarification ensures more reliable application of cosine similarity in AI models, impacting the trustworthiness and performance of downstream applications relying on embedding comparisons.
The understanding of how to correctly apply and interpret cosine similarity in the context of learned embeddings from matrix factorization models is refined, promoting more robust model development.
- · AI/ML Researchers
- · Data Scientists
- · Developers of Embedding Models
- · Practitioners using incompatible training objectives without normalization
Improved stability and interpretability of AI models utilizing embedding similarities.
Faster development cycles for models as foundational issues related to similarity metrics are resolved.
Potentially a subtle but widespread increase in application reliability across various AI-powered products and services.
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Read at arXiv cs.LG