Embedding-based retrieval, also known as dense retrieval, has become the go-to method for modern systems. Neural models map queries and documents to high-dimensional vectors (embeddings) and retrieve documents by nearest-neighbor similarity. However, recent research shows a surprising weakness: single-vector embeddings have a fundamental capacity limit. In short, an embedding can only represent a certain number […]
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