Dual Encoder vs Cross-Encoder: Why Your RAG Pipeline Needs Both
The article discusses the importance of using both Dual Encoders and Cross-Encoders in a Retrieval-Augmented Generation (RAG) pipeline. It highlights the limitations of single-stage retrieval systems, which prioritize speed over accuracy, leading to imprecise results. By implementing a two-stage pipeline, the combination of both models can enhance retrieval precision while maintaining efficiency.
- ▪Single-stage retrieval systems often yield results that are topically related but not precisely relevant.
- ▪A Dual Encoder uses two separate transformer networks to encode queries and documents, allowing for fast retrieval.
- ▪A Cross-Encoder processes the query and document together, providing a more nuanced understanding of relevance.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3469426) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Krunal Kanojiya Posted on May 27 • Originally published at krunalkanojiya.com Dual Encoder vs Cross-Encoder: Why Your RAG Pipeline Needs Both #nlp #python #rag #tutorial My RAG pipeline looked fine on paper. Fast retrieval. Decent cosine scores. But when I tested it with real queries, the top results were always a little off. Documents that shared vocabulary with the query kept showing up instead of documents that actually answered it. The model was doing its job.
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