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New methods slash memory use for learned sparse retrieval indexes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods to significantly reduce the memory requirements of learned sparse retrieval indexes without compromising retrieval efficiency. The proposed techniques involve replacing per-block summaries with document medoids in the inverted index and compressing both components and values in the forward index. These advancements, demonstrated with integration into KANNOLO and evaluated on MS MARCO, show up to a 5.3x speed increase and a 3x reduction in memory usage compared to existing methods. AI

IMPACT These index compression techniques could lead to more efficient and accessible large-scale information retrieval systems.

RANK_REASON The cluster contains an academic paper detailing novel technical methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New methods slash memory use for learned sparse retrieval indexes

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The cluster contains an academic paper detailing novel technical methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Rossano Venturini ·

    Compact and Efficient Indexes for Learned Sparse Retrieval

    This paper investigates how to substantially reduce the memory footprint of learned sparse retrieval indexes without sacrificing the efficiency of state-of-the-art retrieval data structures. Building on SEISMIC, we revisit both levels of its design: the inverted index used to sel…

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