When recall plateaus: the late-interaction technique most teams skip
The article discusses the late-interaction technique that can significantly improve retrieval recall in machine learning models. A case study illustrates how a team increased their recall from 58% to 81% by implementing a reranker instead of fine-tuning their embedding model. The late-interaction method preserves more detailed information by using per-token embeddings rather than averaging them into a single vector.
- ▪A team improved their retrieval recall from 58% to 81% by adding a reranker.
- ▪The late-interaction technique allows for better distinction between concepts in a text chunk.
- ▪ColBERT is a method that keeps per-token embeddings to enhance relevance scoring.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3948393) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } SapotaCorp Posted on May 24 • Originally published at sapotacorp.vn on May 24 When recall plateaus: the late-interaction technique most teams skip #ragsystems A founder we work with had been stuck on the same problem for two months. Their RAG retrieval recall was sitting at 58%. They had tried OpenAI's embedding-3-small, then embedding-3-large, then BGE-M3, then Voyage. Each swap added a couple of points, then the curve flattened.
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