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Single-vector embedding models can be used to extract sparse latent terms, and BM25 can turn this vocabulary into a strong retriever.
SMART is a framework that unlocks latent multi-vector capabilities in single-vector models for multimodal retrieval, improving state-of-the-art performance with reduced computational costs via contrastive training and late-interaction inference.