11 citations · 16 across the 4 of their papers we have counts for
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cs.IR2023★ 4 cited
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…
cs.IR2023★ 1 cited
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…
cs.IR2023★ 11 cited
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…