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Computer Science > Machine Learning

arXiv:2505.12540 (cs)
[Submitted on 18 May 2025 (v1), last revised 20 May 2025 (this
version, v2)]

Title:Harnessing the Universal Geometry of Embeddings

Authors:Rishi Jha, Collin Zhang, Vitaly Shmatikov, John X. Morris
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Embeddings, by Rishi Jha and 3 other authors
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    Abstract:We introduce the first method for translating text
    embeddings from one vector space to another without any paired
    data, encoders, or predefined sets of matches. Our unsupervised
    approach translates any embedding to and from a universal latent
    representation (i.e., a universal semantic structure conjectured
    by the Platonic Representation Hypothesis). Our translations
    achieve high cosine similarity across model pairs with different
    architectures, parameter counts, and training datasets.
    The ability to translate unknown embeddings into a different
    space while preserving their geometry has serious implications
    for the security of vector databases. An adversary with access
    only to embedding vectors can extract sensitive information about
    the underlying documents, sufficient for classification and
    attribute inference.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2505.12540 [cs.LG]
          (or arXiv:2505.12540v2 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2505.12540
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Rishi Jha [view email]
[v1] Sun, 18 May 2025 20:37:07 UTC (3,179 KB)
[v2] Tue, 20 May 2025 15:38:41 UTC (3,180 KB)
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