EigenLI: Spectral Approximations to Late Interaction
Late-interaction models such as ColBERT achieve strong effectiveness by representing each document with many token-level vectors, but this expressivity leads to large indexing cost, storage footprints and expensive MaxSim scoring. We show that late-interaction representations exhibit an intrinsic low-rank structure: document token embeddings concentrate in a low-dimensional subspace that preserves most of the retrieval signal. Leveraging this observation, we introduce EigenLI, a spectral approximation framework that compresses late-interaction representations via document-specific low-dimensional subspaces. Unlike clustering or pooling methods, EigenLI identifies the dominant eigendirecti...
| Authors | Archish S, Sabyasachi Basu, Ankit Garg et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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