Improving RAG Systems with Hybrid Retrieval
May 15, 2024
Retrieval-Augmented Generation (RAG) has become a cornerstone technique for enhancing large language models with external knowledge.
Traditional RAG systems typically rely on either dense retrieval (embedding-based similarity) or sparse retrieval (keyword-based methods like BM25).
By combining both approaches, we can create a more robust retrieval system that leverages the strengths of each method.
Dense retrievers excel at semantic understanding but may miss exact keyword matches, while sparse retrievers are great at finding specific terms but lack semantic comprehension.
In our experiments, implementing a hybrid retrieval system using RRF fusion between DPR (Dense Passage Retrieval) and BM25 improved retrieval accuracy by 18%.
Introduction
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Main Content
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Conclusion
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