Efficient Constant-Space Multi-Vector Retrieval

Multi-vector retrieval methods, exemplified by the ColBERT architecture, have shown substantial promise for retrieval by providing strong trade-offs in terms of retrieval latency and effectiveness. However, they come at a high cost in terms of storage since a (potentially compressed) vector needs to be stored for every token in the input collection. To overcome this issue, we propose encoding documents to a fixed number of vectors, which are no longer necessarily tied to the input tokens. Beyond reducing the storage costs, our approach has the advantage that document representations become of a fixed size on disk, allowing for better OS paging management. Through experiments using the MSMARCO passage corpus and BEIR with the ColBERT-v2 architecture, a representative multi-vector ranking model architecture, we find that passages can be effectively encoded into a fixed number of vectors while retaining most of the original effectiveness.

@article{macavaney2025_2504.01818,
  title={ Efficient Constant-Space Multi-Vector Retrieval },
  author={ Sean MacAvaney and Antonio Mallia and Nicola Tonellotto },
  journal={arXiv preprint arXiv:2504.01818},
  year={ 2025 }
}