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The author shares their work on reducing the cost of multi-vector retrieval by using k-means as top-1 sparse coding. Omar Khattab adds that late-interaction sparse retrieval with neuron-level inverted indexing on unsupervised sparse autoencoders works well.
This paper proposes Single-stage Sparse Retrieval (SSR), which replaces K-means clustering with sparse autoencoders and inverted indexing, achieving 15x faster indexing and halved retrieval latency while improving accuracy on the BEIR benchmark.