  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.
