🤖 AI Summary
To address the low online top-k document retrieval efficiency in learned sparse retrieval, this paper proposes Dynamic Superblock Pruning (DSP). DSP introduces a hierarchical superblock structure—departing from conventional flat blocks or clusters—and models the hierarchical relationships among document blocks to enable early, group-level pruning at the superblock granularity. It further integrates a dynamic threshold-driven selection and pruning strategy, ensuring rank-safety or controllable approximation accuracy under high-relevance competition constraints. The method is fully optimized for single-threaded CPU execution, requiring no specialized hardware. Evaluated on the MS MARCO passage collection, DSP significantly outperforms state-of-the-art sparse retrieval baselines: it achieves substantial speedup in online retrieval while maintaining high recall.
📝 Abstract
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 sequence of document blocks and conducts a superblock-level selection to decide if some superblocks can be pruned before visiting their child blocks. SP generalizes the previous flat block or cluster-based pruning, allowing the early detection of groups of documents that cannot or are less likely to appear in the final top-k list. SP can accelerate sparse retrieval in a rank-safe or approximate manner under a high-relevance competitiveness constraint. Our experiments show that the proposed scheme significantly outperforms state-of-the-art baselines on MS MARCO passages on a single-threaded CPU.