Collin Zhang

Cornell's vec2vec translates embeddings between models it has never seen

Cornell researchers' vec2vec translates text embeddings between models with no paired data or encoder access, hitting up to 0.92 cosine similarity and 100% top-1 accuracy, and recovering readable info from up to 80% of test emails via zero-shot inversion.