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New MV-IVF index boosts multi-vector retrieval efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MV-IVF, a new inverted-file index designed for multi-vector retrieval. This system utilizes a novel clustering algorithm, MV-Lloyd, to efficiently index sets of embeddings, which represent queries and documents. MV-IVF aims to improve retrieval quality and speed by directly indexing multi-vector data, unlike previous methods that relied on proxy representations or filter-and-rerank pipelines. The system demonstrates significant improvements in latency and throughput on text retrieval benchmarks. AI

IMPACT Introduces a more efficient method for multi-vector retrieval, potentially improving search performance in large-scale information retrieval systems.

RANK_REASON The cluster describes a new algorithm and index structure for information retrieval, presented in an arXiv paper. [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 MV-IVF index boosts multi-vector retrieval efficiency

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The cluster describes a new algorithm and index structure for information retrieval, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 Română(RO) · Rajesh Jayaram ·

    MV-IVF: Multi-Vector Retrieval via Multi-Vector Clustering

    Multi-vector retrieval represents each query and document as a set of embeddings and scores relevance via fine-grained set-to-set interactions such as MaxSim (Chamfer similarity). This expressiveness improves retrieval quality over single-vector representations, but makes search …

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