11 citations · 11 across the 5 of their papers we have counts for
6 papers
Adaptive Sparsity Optimization with Learnable Soft Top-K and Per-Term Thresholding for Efficient Retrieval
Wentai Xie, Parker Carlson, Shanxiu He +1
Recent work on neural sparse retrieval has demonstrated strong relevance by leveraging Large Language Models (LLMs) for semantic term expansion. However, learned models paired with…
Dynamic Superblock Pruning for Fast Learned Sparse Retrieval
Parker Carlson, Wentai Xie, Shanxiu He +1
This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. SP structures the sparse index as a set of superblocks on a s…
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…
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…
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…
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…