Efficient Document Ranking with Learnable Late Interactions

arXiv:2406.17968v1 Announce Type: cross
Abstract: 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 …

abstract arxiv benefits cross-encoder cs.ai cs.ir cs.lg document document embeddings embeddings encoder fundamental information interactions latency quality query ranking retrieval stat.ml type while