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New method recovers retrieval quality lost to document vector compression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to improve the quality of compressed document vectors in late-interaction models. By learning a global linear correction, the method can recover retrieval quality lost due to aggressive compression without retraining the encoder or recompressing documents. This approach was evaluated using ColBERTv2 and Jina v4 on MS MARCO and BEIR benchmarks, as well as ColPali2 on the ViDoRe v3 visual document retrieval task. The correction demonstrated significant improvements in retrieval quality across various compression methods and datasets. AI

IMPACT This research could lead to more efficient storage and retrieval of information in large-scale document indexing systems.

RANK_REASON The cluster contains a research paper detailing a new method for improving information retrieval models. [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 method recovers retrieval quality lost to document vector compression

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The cluster contains a research paper detailing a new method for improving information retrieval models. [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) · Viraj Agrawal ·

    Simple Linear Corrections for Compression in Late Interaction

    Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and dense encoders in terms of quality and cost of embedding. However, compared to the latter, the indices are costly to store and score…

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