Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness

Multi-vector retrieval has become a key approach in enhancing the accuracy of dense retrieval models, outperforming traditional single-vector and sparse retrieval methods by capturing fine-grained interactions through models like ColBERT, ColPali, and MUVERA. Despite its effectiveness, multi-vector retrieval demands higher storage and computational resources, leading to increased memory usage and query-time latency. However, it remains more efficient than cross-encoder rerankers by precomputing document representations and employing late interaction mechanisms. ConstBERT, a model developed by Pinecone and academic collaborators, offers a practical solution by using fixed-size document representations, reducing storage overhead while maintaining effectiveness. This approach allows multi-vector retrieval to function as an efficient intermediate step within a cascading retrieval pipeline, balancing speed and accuracy by employing progressively sophisticated models at different stages. By integrating ConstBERT into systems like Pinecone, users can achieve scalable, efficient, and accurate search processes, leveraging metadata-based reranking to enhance retrieval performance without significant storage costs.