PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, cross-language, and multilingual retrieval. PLAID differs from ColBERT by assigning terms to clusters and representing those terms as cluster centroids plus compressed residual vectors. While PLAID is effective in batch experiments, its performance degrades in streaming settings where documents arrive over time because representations of new tokens may be poorly modeled by the earlier tokens used to select cluster centroids. PLAID Streaming Hierarchical Indexing that Runs on Terabytes of Temporal Text (PLAID SHIRTTT) addresses this concern using multi-phase incremental indexing based on hierarchical sharding. Experiments on ClueWeb09 and the multilingual NeuCLIR collection demonstrate the effectiveness of this approach both for the largest collection indexed to date by the ColBERT architecture and in the multilingual setting, respectively.

翻译：PLAID是一种利用预训练语言模型实现ColBERT延迟交互双编码器的高效实现方案，在单语、跨语言和多语言检索任务中持续取得最先进性能。与ColBERT不同，PLAID通过将词项分配到聚类中，并将这些词项表示为聚类质心加上压缩残差向量。尽管PLAID在批量实验中表现优异，但在文档随时间到达的流式场景中，其性能会出现下降，因为新词元的表示可能被用于选择聚类质心的早期词元所误建模。基于分层分片的多阶段增量索引技术——PLAID流式分层索引（PLAID SHIRTTT）有效解决了这一问题。在ClueWeb09数据集和多语言NeuCLIR语料库上的实验分别证明了该方法在ColBERT架构迄今索引的最大规模语料库以及多语言场景中的有效性。