用量化压缩ColBERT索引，体积减半仍保持检索效果
ColBERTSaR: Sparsified ColBERT Index via Product Quantization
- 用乘积量化将稠密嵌入转为稀疏倒排索引
- 索引大小比1比特PLAID小50%-70%，检索性能不变
- 适合需要高效存储的实时检索系统
尽管ColBERT是一种高效的神经检索架构，但其依赖庞大的索引结构来支持候选集检索，包括近似标记嵌入的聚集、解压文档标记嵌入以及执行MaxSim操作。PLAID等类似实现的索引占用原始文本5到10倍的磁盘空间，限制了可扩展性。此外，已有研究指出，查询时的聚集与解压阶段是主要性能瓶颈。通过阈值筛选和分数近似虽能减少需聚集的文档标记数，但仍需完整索引支持即席查询。本文提出一种嵌入量化方法，将ColBERT索引转化为真正的倒排索引。理论上，量化后的ColBERT等价于学习型稀疏检索，仅评分机制不同。实证表明，本方法索引大小比1比特PLAID小50%-70%，同时保持检索有效性。
原文摘要 · Abstract (English)
While ColBERT is an effective neural retrieval architecture, it requires a heavy index structure to support candidate set retrieval based on approximated token embeddings, gathering and decompressing document token embeddings, and applying the MaxSim operation. Indexes in PLAID and similar ColBERT implementations require five to ten times the disk storage of the original raw text, which limits their scalability. Furthermore, prior work has identified that the gathering and decompression stages are the primary inefficiencies at query time. Limiting the number of document tokens that must be gathered by thresholding and score approximation does not eliminate the need for the entire index to support ad hoc queries. In this work, we propose an embedding quantization approach that turns a ColBERT index into a true inverted index. We show that, theoretically, ColBERT with embedding quantization is equivalent to learned-sparse retrieval except for the scoring mechanism. Empirically, we demonstrate that our index is 50-70% smaller than a one-bit PLAID index while retaining retrieval effectiveness.
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