Harnessing the Universal Geometry of Embeddings
Summary
A new unsupervised method translates embeddings across different vector spaces without paired data, enabling a universal latent representation. The approach preserves geometry across model architectures and shows high cosine similarity in translations, but it also raises security and privacy concerns since embedding vectors could leak sensitive information about underlying documents.