We review MUVERA, a novel algorithm designed to significantly improve the efficiency of multi-vector information retrieval. Traditional information retrieval often uses single-vector embeddings, which are computationally fast but less accurate than multi-vector models like ColBERT. While multi-vector models offer enhanced accuracy by representing data points with sets of embeddings, their complex similarity scoring leads to substantial computational costs. MUVERA addresses this challenge by transforming multi-vector retrieval into a simpler single-vector search problem using Fixed Dimensional Encodings (FDEs), which are single vectors approximating multi-vector similarity. This approach allows MUVERA to leverage highly optimized maximum inner product search (MIPS) algorithms, resulting in faster retrieval times with minimal accuracy loss, even outperforming prior state-of-the-art methods like PLAID. The research provides theoretical guarantees for FDEs and demonstrates their effectiveness across various information retrieval datasets.

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