Dense Passage Retrieval (DPR): Semantic Search for QA
DPR finds answers by meaning, not just keywords. It converts questions and documents into vectors and finds the closest matches, forming the core of Retrieval-Augmented Generation (RAG).
Dense Passage Retrieval (DPR) swaps keyword matching for semantic search. Instead of counting words like TF-IDF, it uses models to encode questions and document passages into dense vectors. The most relevant passages are those closest in vector space, forming the 'Retrieval' in RAG systems. The biggest footgun is that retrieval quality is tied to the embedding model; a generic model may fail to understand domain-specific language, returning irrelevant context.
Read the original → huggingface.co
- #llm
- #rag
- #information retrieval
- #embeddings
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