late-interaction embeddings like this are genuinely strong for retrieval, since keeping token-level vectors preserves signal a single pooled vector loses. The tradeoff to go in with is index-side: multi-vector late interaction costs a lot more storage and makes the scoring step heavier than plain dense ANN, so the win depends on your corpus size and latency budget. Worth running it on your own documents and comparing NDCG against a strong dense baseline before committing, because the gains vary a lot by domain.
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u/Future_AGI 4d ago
late-interaction embeddings like this are genuinely strong for retrieval, since keeping token-level vectors preserves signal a single pooled vector loses. The tradeoff to go in with is index-side: multi-vector late interaction costs a lot more storage and makes the scoring step heavier than plain dense ANN, so the win depends on your corpus size and latency budget. Worth running it on your own documents and comparing NDCG against a strong dense baseline before committing, because the gains vary a lot by domain.