Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental
approaches for query-document relevance in information retrieval. To predict
relevance, CE models use joint query-document embeddings, while DE models
maintain factorized query and document embeddings; usually, the former has
higher quality while the latter benefits from lower latency. Recently,
late-interaction models have been proposed to realize more favorable
latency-quality tradeoffs, by using a DE structure followed by a lightweight
scorer based on query and document token embeddings. However, these lightweight
scorers are often hand-crafted, and there is no understanding of their
approximation power; further, such scorers require access to individual
document token embeddings, which imposes an increased latency and storage
burden. In this paper, we propose novel learnable late-interaction models
(LITE) that resolve these issues. Theoretically, we prove that LITE is a
universal approximator of continuous scoring functions, even for relatively
small embedding dimension. Empirically, LITE outperforms previous
late-interaction models such as ColBERT on both in-domain and zero-shot
re-ranking tasks. For instance, experiments on MS MARCO passage re-ranking show
that LITE not only yields a model with better generalization, but also lowers
latency and requires 0.25x storage compared to ColBERT.

提出了一种新颖的学习型后交互模型（LITE），通过解决轻量级评分器的手工设计和访问个体文档令牌嵌入的问题来改善查询 - 文档关联性在信息检索中的性能，并在领域内和零样本重排序任务上优于以前的后交互模型。