Text Embeddings are All Alike
a year ago
- #Embeddings
- #Machine Learning
- #Security
- Introduces an unsupervised method for translating text embeddings between different vector spaces without paired data or predefined matches.
- Proposes a universal latent representation for embeddings, aligning with the Platonic Representation Hypothesis.
- Achieves high cosine similarity across diverse model architectures and training datasets.
- Highlights security implications for vector databases, as adversaries can infer sensitive information from embeddings.