5 papers · 1 filter
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…
Efficiency Optimizations for Superblock-based Sparse Retrieval
Parker Carlson, Wentai Xie, Rohil Shah +1
Learned sparse retrieval (LSR) is a popular method for first-stage retrieval because it combines the semantic matching of language models with efficient CPU-friendly algorithms. Pr…
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…
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…