@mixedbreadai: By now, everyone knows that single-vector embedding models are hugely limiting for modern workflows. But they contain t…
Summary
Single-vector embedding models can be used to extract sparse latent terms, and BM25 can turn this vocabulary into a strong retriever.
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Cached at: 06/03/26, 01:40 AM
By now, everyone knows that single-vector embedding models are hugely limiting for modern workflows.
But they contain than you think: you can extract sparse Latent Terms from them.
And it turns out that BM25 is all you need to turn this vocabulary into a strong retriever. https://t.co/rfAbLQnspQ
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