WARP: An Efficient Engine for Multi-Vector Retrieval

• URL: http://arxiv.org/abs/2501.17788v2
• Date: Wed, 30 Apr 2025 08:28:56 GMT
• Title: WARP: An Efficient Engine for Multi-Vector Retrieval
• Authors: Jan Luca Scheerer, Matei Zaharia, Christopher Potts, Gustavo Alonso, Omar Khattab,
• Abstract summary: WARP is a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective.<n>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.
• Score: 42.128201454569165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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