Efficient Multi-Vector Dense Retrieval Using Optimized Bit Vectors and Product Quantization

Core Concepts

This paper introduces EMVB, a novel framework for efficient query processing in multi-vector dense retrieval. EMVB employs a highly efficient pre-filtering step using optimized bit vectors, a column-wise SIMD reduction for candidate passage retrieval, and a late interaction mechanism that combines product quantization with per-document term filtering to significantly improve the efficiency of multi-vector dense retrieval systems.

Abstract

The paper presents EMVB, a novel framework for efficient multi-vector dense retrieval, which advances the state-of-the-art PLAID approach. EMVB introduces four key contributions: Pre-filtering of candidate passages: EMVB employs a highly efficient pre-filtering step using optimized bit vectors to quickly discard non-relevant passages, significantly speeding up the candidate passage filtering phase. Efficient centroid interaction: EMVB computes the centroid interaction in a more efficient manner by leveraging SIMD instructions for column-wise max reduction, reducing the latency of this step. Late interaction with Product Quantization: EMVB uses Product Quantization (PQ) to reduce the memory footprint of storing vector representations while enabling fast late interaction, providing up to 3.6x speedup compared to PLAID's residual compression. Per-document term filtering for late interaction: EMVB introduces a dynamic per-document term filtering approach for the late interaction phase, further improving efficiency by up to 30%. The authors evaluate EMVB against PLAID on the MS MARCO and LoTTE datasets. Results show that EMVB is up to 2.8x faster than PLAID on the in-domain MS MARCO dataset, while reducing the memory footprint by 1.8x with no loss in retrieval quality. On the out-of-domain LoTTE dataset, EMVB offers up to 2.9x speedup with minimal retrieval quality degradation.

Stats

The MS MARCO dataset used in the experiments contains about 600M d-dimensional vectors, with d = 128. EMVB reduces the memory footprint by 1.8x compared to PLAID on the MS MARCO dataset.

Quotes

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Key Insights Distilled From

Efficient Multi-Vector Dense Retrieval Using Bit Vectors

by Franco Maria... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02805.pdf

Deeper Inquiries

How can the pre-filtering approach in EMVB be extended to other dense retrieval systems beyond multi-vector techniques

The pre-filtering approach in EMVB can be extended to other dense retrieval systems by adapting the concept of optimized bit vectors for efficient candidate filtering. This approach can be applied to systems that involve large-scale embeddings or token-level representations, regardless of whether they are multi-vector techniques or not. By implementing a similar pre-filtering mechanism based on optimized bit vectors, other dense retrieval systems can benefit from reduced computational costs and improved efficiency in candidate selection. This extension can enhance the overall performance of various retrieval systems by streamlining the initial filtering process and reducing the number of computations required for candidate selection.

What are the potential trade-offs between the efficiency gains and retrieval quality when using more aggressive filtering approaches like the per-document term filtering in EMVB

When using more aggressive filtering approaches like the per-document term filtering in EMVB, there are potential trade-offs between efficiency gains and retrieval quality. The aggressive filtering may lead to a reduction in the number of candidate passages considered during the retrieval process, which can significantly improve the overall efficiency by reducing computational overhead. However, this approach may also result in a loss of retrieval quality, especially if relevant passages are filtered out due to stringent criteria. The trade-off lies in finding the right balance between efficiency and quality, where the filtering criteria need to be optimized to ensure that irrelevant passages are excluded while retaining the necessary diversity and relevance in the retrieved results.

How could the insights from EMVB be applied to improve the efficiency of other information retrieval tasks beyond passage retrieval, such as document or entity retrieval

The insights from EMVB can be applied to improve the efficiency of other information retrieval tasks beyond passage retrieval, such as document or entity retrieval, by leveraging similar optimization techniques. For document retrieval, the pre-filtering approach based on optimized bit vectors can be adapted to efficiently select relevant documents from a large corpus, reducing the computational burden and improving retrieval speed. Additionally, techniques like Product Quantization (PQ) can be utilized to reduce the memory footprint of storing vector representations in various retrieval tasks, enabling faster late interaction phases without compromising accuracy. By incorporating these insights into different retrieval tasks, the overall efficiency and effectiveness of the retrieval systems can be enhanced.