Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing

TL;DR

TACHIOM is a novel multivector retrieval system that leverages token-aware clustering and hierarchical indexing to significantly improve speed and scalability while maintaining high effectiveness.

Contribution

The paper introduces TACHIOM, a scalable, token-aware clustering method that accelerates multivector retrieval and enables efficient indexing for large datasets.

Findings

TACHIOM achieves up to 247x faster clustering than k-means.

It provides up to 9.8x faster retrieval speed over state-of-the-art systems.

Maintains comparable or superior effectiveness on benchmark datasets.

Abstract

Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substantial computational and memory costs. Current solutions, based on the well-known k-means clustering algorithm, group similar vectors together to enable both effective compression and efficient retrieval. However, standard k-means scales poorly with the number of clusters and dataset size, and favours frequent tokens during training while underrepresenting rare, discriminative ones. In this work, we introduce TACHIOM, a multivector retrieval system that exploits token-level structure to significantly accelerate both clustering and retrieval. By accounting for tokens' distribution during centroid allocation, TACHIOM easily scales to millions of centroids, enabling highly accurate document scoring using only centroids, avoiding expensive token-level computation. TACHIOM combines a graph-based index over centroids with an optimized Product Quantization layout for efficient final scoring. Experiments on MS-MARCOv1 and LoTTE show that TACHIOM achieves up to $247\times$ faster clustering than k-means and up to $9.8\times$ retrieval speedup over state-of-the-art systems while maintaining comparable or superior effectiveness.