13 citations · 44 across the 11 of their papers we have counts for
4 papers · 1 filter
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
Minghan Li, Honglei Zhuang, Kai Hui +5
Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, cross-encoder rankers remains under-explored. A rec…
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval
Sheng-Chieh Lin, Akari Asai, Minghan Li +5
Various techniques have been developed in recent years to improve dense retrieval (DR), such as unsupervised contrastive learning and pseudo-query generation. Existing DRs, however…
Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction
Xinyu Zhang, Minghan Li, Jimmy Lin
Recent progress in information retrieval finds that embedding query and document representation into multi-vector yields a robust bi-encoder retriever on out-of-distribution datase…
SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes
Minghan Li, Sheng-Chieh Lin, Xueguang Ma +1
This paper introduces Sparsified Late Interaction for Multi-vector (SLIM) retrieval with inverted indexes. Multi-vector retrieval methods have demonstrated their effectiveness on v…