Harnessing the Universal Geometry of Embeddings

The authors present an unsupervised method that maps text embeddings between arbitrary vector spaces via a universal latent representation, requiring no paired data, encoders, or predefined matches. Experiments show high cosine similarity across models with differing architectures, sizes, and training corpora, and reveal that an attacker with only embedding vectors can infer document classifications and attributes, threatening vector-database security.