Papers by Yingrui Yang

2 papers
Threshold-driven Pruning with Segmented Maximum Term Weights for Approximate Cluster-based Sparse Retrieval (2024.emnlp-main)

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Challenge: Using rank score thresholding, sparse retrieval skips the index at cluster and document levels.
Approach: They propose a pruning control scheme with a probabilistic guarantee on rank-safeness competitiveness.
Outcome: The proposed pruning control scheme improves accuracy and safeness while delivering low latency on single-threaded CPU.
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking (2022.acl-long)

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Challenge: Recent work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of a large online storage.
Approach: They propose to decouple document-specific and document-independent ranking contributions during codebook-based compression for effective online decompression and embedding composition for better search relevance.
Outcome: The proposed model achieves high relevance and space efficiency with minimal computational cost and low computational complexity.

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