让多向量检索快10倍，同时保持高精度。
LEMUR: Learned Multi-Vector Retrieval
- 将多向量相似度搜索转为单层神经网络学习问题
- 推理速度比之前方法快一个数量级
- 可直接用现有单向量索引加速，适合工业检索系统
晚交互模型（如 ColBERT）生成的多向量表示在信息检索中表现优于单向量表示。在多向量检索系统中，查询和文档按词元分别生成嵌入向量，相似度通过 MaxSim 度量。然而，这种高精度带来显著增加的搜索延迟。本文提出 LEMUR，一种高效多向量相似度搜索框架。LEMUR 包含两个连续的简化步骤：首先，将多向量相似度搜索建模为监督学习问题，可用单隐藏层神经网络求解；其次，将该模型下的推理转化为其隐空间中的单向量相似度搜索，从而可复用现有单向量索引实现加速。LEMUR 的检索速度比先前方法快一个数量级。代码已开源。
原文摘要 · Abstract (English)
Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vector representations in information retrieval applications. In multi-vector retrieval systems, both queries and documents are encoded using one embedding per token, and similarity between queries and documents is measured by the MaxSim similarity measure. However, the improved quality of multi-vector retrieval comes at the expense of significantly increased search latency. In this work, we introduce LEMUR, a simple yet efficient framework for multi-vector similarity search. LEMUR consists of two consecutive problem reductions: First, we formulate multi-vector similarity search as a supervised learning problem that can be solved using a one-hidden-layer neural network. Second, we reduce inference under this model to single-vector similarity search in its latent space, enabling the use of existing single-vector search indexes to accelerate retrieval. LEMUR is an order of magnitude faster than prior multi-vector similarity search methods. Our code is available at https://github.com/ejaasaari/lemur
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