activity
20182025
most citedOptimizing Guided Traversal for Fast Learned Sparse Retrieval

16 citations · 31 across the 10 of their papers we have counts for

collaborators
Showing cs.IRShow all

8 papers · 1 filter

cs.IR2025

LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval

Yingrui Yang, Parker Carlson, Yifan Qiao +3

This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical…

cs.IR2024

Weighted KL-Divergence for Document Ranking Model Refinement

Yingrui Yang, Yifan Qiao, Shanxiu He +1

Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning. A tight distribution…

cs.IR2024

Approximate Cluster-Based Sparse Document Retrieval with Segmented Maximum Term Weights

Yifan Qiao, Shanxiu He, Yingrui Yang +2

This paper revisits cluster-based retrieval that partitions the inverted index into multiple groups and skips the index partially at cluster and document levels during online infer…

cs.IR2023★ 11 cited

Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval

Yifan Qiao, Yingrui Yang, Shanxiu He +1

Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency. This paper descr…

cs.IR2023★ 16 cited

Optimizing Guided Traversal for Fast Learned Sparse Retrieval

Yifan Qiao, Yingrui Yang, Haixin Lin +1

Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpac…

cs.IR2022★ 3 cited

Dual Skipping Guidance for Document Retrieval with Learned Sparse Representations

Yifan Qiao, Yingrui Yang, Haixin Lin +3

This paper proposes a dual skipping guidance scheme with hybrid scoring to accelerate document retrieval that uses learned sparse representations while still delivering a good rele…