research-article

Efficient Document-at-a-time and Score-at-a-time Query Evaluation for Learned Sparse Representations

Authors:

Joel Mackenzie,

Andrew Trotman,

Jimmy LinAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 96, Pages 1 - 28

https://doi.org/10.1145/3576922

Published: 22 March 2023 Publication History

Abstract

Researchers have had much recent success with ranking models based on so-called learned sparse representations generated by transformers. One crucial advantage of this approach is that such models can exploit inverted indexes for top-k retrieval, thereby leveraging decades of work on efficient query evaluation. Yet, there remain many open questions about how these learned representations fit within the existing literature, which our work aims to tackle using four representative learned sparse models. We find that impact weights generated by transformers appear to greatly reduce opportunities for skipping and early exiting optimizations in well-studied document-at-a-time (DaaT) approaches. Similarly, “off-the-shelf” application of score-at-a-time (SaaT) processing exhibits a mismatch between these weights and assumptions behind accumulator management strategies. Building on these observations, we present solutions to address deficiencies with both DaaT and SaaT approaches, yielding substantial speedups in query evaluation. Our detailed empirical analysis demonstrates that both methods lie on the effectiveness–efficiency Pareto frontier, indicating that the optimal choice for deployment depends on operational constraints.

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Cited By

Moffat AMackenzie JMallia APetri MSakai TIshita EOhshima HHasibi FMao JJose J(2024)Rank-Biased Quality Measurement for Sets and RankingsProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698405(135-144)Online publication date: 8-Dec-2024
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Index Terms

Efficient Document-at-a-time and Score-at-a-time Query Evaluation for Learned Sparse Representations
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 22 March 2023

Online AM: 15 December 2022

Accepted: 28 November 2022

Revised: 18 October 2022

Received: 13 April 2022

Published in TOIS Volume 41, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3673791.3698405
Bruch SNardini FIngber ALiberty E(2024)Bridging Dense and Sparse Maximum Inner Product SearchACM Transactions on Information Systems10.1145/366532442:6(1-38)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665324
Mallia ASuel TTonellotto NHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Faster Learned Sparse Retrieval with Block-Max PruningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657906(2411-2415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657906
Mansour WZhuang SZuccon GMackenzie JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Revisiting Document Expansion and Filtering for Effective First-Stage RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657850(186-196)Online publication date: 10-Jul-2024
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Lassance CDejean HClinchant STonellotto N(2024)Two-Step SPLADE: Simple, Efficient and Effective Approximation of SPLADEAdvances in Information Retrieval10.1007/978-3-031-56060-6_23(349-363)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_23
Mackenzie JZhuang SZuccon GYoshioka MKiseleva JAliannejadi M(2023)Exploring the Representation Power of SPLADE ModelsProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605129(143-147)Online publication date: 9-Aug-2023
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Lassance CLupart SDéjean HClinchant STonellotto NChen HDuh WHuang HKato MMothe JPoblete B(2023)A Static Pruning Study on Sparse Neural RetrieversProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591941(1771-1775)Online publication date: 19-Jul-2023
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Bruch SMackenzie JMaistro MNardini FChen HDuh WHuang HKato MMothe JPoblete B(2023)ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591922(3456-3459)Online publication date: 19-Jul-2023
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