不训练的多向量压缩方法中，合并优于剪枝。
A Brief Comparison of Training-Free Multi-Vector Sequence Compression Methods
- 用无训练方法压缩多向量表示中的分词维度
- 合并策略在减小索引大小的同时保持检索效果
- 适合关注部署效率的检索系统研发者
尽管多向量检索模型在检索质量上优于同等规模的单向量模型，但其实际应用受限于更大的索引尺寸，这源于文档嵌入中额外的序列长度维度。由于文档嵌入大小直接决定内存开销和查询延迟，压缩对部署至关重要。本文评估了针对多向量检索特有分词序列长度的无训练压缩方法。结果表明，在减少索引大小的同时保持检索有效性方面，分词合并严格优于分词剪枝。
原文摘要 · Abstract (English)
While multi-vector retrieval models outperform single-vector models of comparable size in retrieval quality, their practicality is limited by substantially larger index sizes, driven by the additional sequence-length dimension in their document embeddings. Because document embedding size dictates both memory overhead and query latency, compression is essential for deployment. In this work, we present an evaluation of training-free methods targeting the token sequence length, a dimension unique to multi-vector retrieval. Our findings suggest that token merging is strictly superior to token pruning for reducing index size while maintaining retrieval effectiveness.
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