WARP: An Efficient Engine for Multi-Vector Retrieval
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916828194799616 |
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| author | Scheerer, Jan Luca Zaharia, Matei Potts, Christopher Alonso, Gustavo Khattab, Omar |
| author_facet | Scheerer, Jan Luca Zaharia, Matei Potts, Christopher Alonso, Gustavo Khattab, Omar |
| contents | Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARP$_\text{SELECT}$ for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WARP: An Efficient Engine for Multi-Vector Retrieval Scheerer, Jan Luca Zaharia, Matei Potts, Christopher Alonso, Gustavo Khattab, Omar Information Retrieval Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARP$_\text{SELECT}$ for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality. |
| title | WARP: An Efficient Engine for Multi-Vector Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2501.17788 |