@yifeiwang77: Thanks for sharing our work @lateinteraction @sum! The idea is extremely simple: - multi-vector retrieval is so costly …
Summary
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.
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Cached at: 05/31/26, 12:52 AM
Thanks for sharing our work @lateinteraction @sum!
The idea is extremely simple:
- multi-vector retrieval is so costly that it often needs k-means to categorize tokens first;
- k-means is essentially a top-1 sparse coding method (n-dim dense->1-dim cluster id), which means it
Omar Khattab (@lateinteraction): Late-interaction sparse retrieval? 😁
With neuron-level inverted indexing, on top of unsupervised sparse autoencoders. Works much better than directly training sparse retrievers.
Lots of cool ideas developed & composed in here. Thanks for the insights @Veritas2026 @yifeiwang77!
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