Multi-vector retrieval methods combine the merits of sparse (e.g. BM25) and
dense (e.g. DPR) retrievers and have achieved state-of-the-art performance on
various retrieval tasks. These methods, however, are orders of magnitude slower
and need much more space to store their indices compared to their single-vector
counterparts. In this paper, we unify different multi-vector retrieval models
from a token routing viewpoint and propose conditional token interaction via
dynamic lexical routing, namely CITADEL, for efficient and effective
multi-vector retrieval. CITADEL learns to route different token vectors to the
predicted lexical ``keys'' such that a query token vector only interacts with
document token vectors routed to the same key. This design significantly
reduces the computation cost while maintaining high accuracy. Notably, CITADEL
achieves the same or slightly better performance than the previous state of the
art, ColBERT-v2, on both in-domain (MS MARCO) and out-of-domain (BEIR)
evaluations, while being nearly 40 times faster. Code and data are available at
this https URL.

本文从令牌路由视角统一不同的多向量检索模型，提出通过动态词汇路由的条件令牌交互，即 CITADEL，以实现高效且有效的多向量检索。通过将不同的令牌向量路由到预测的词汇 “关键词”，以便查询令牌向量只与路由到相同关键词的文档令牌向量进行交互，从而显著减少计算成本，同时保持高准确性。值得注意的是，CITADEL 在速度方面几乎快 40 倍，同时在领域内（MS MARCO）和领域外（BEIR）评估中均获得了与之前的最高水平 ColBERT-v2 相同或略好的性能。