2 days ago
高效码农
MUVERA: Revolutionizing Multi-Vector Retrieval Efficiency In the rapidly evolving landscape of information retrieval (IR), neural embedding models have emerged as fundamental tools. These models transform data points into vector embeddings, enabling efficient retrieval through optimized maximum inner product search (MIPS) algorithms. However, the introduction of multi-vector models, such as ColBERT, has presented new challenges in terms of computational complexity and retrieval efficiency. The Promise and Peril of Multi-Vector Models Multi-vector models represent a significant advancement in IR technology. Unlike single-vector models that produce one embedding per data point, multi-vector models generate multiple embeddings. This approach has demonstrated superior performance in …
