What is Dense Retrieval?
A retrieval method that uses dense vector embeddings, enabling semantic search and advanced contextual retrieval.
More about Dense Retrieval:
Dense Retrieval uses dense vector embeddings to match queries with documents based on semantic similarity, rather than relying on exact term matching. Models like bi-encoders and cross-encoders are often employed to create these embeddings.
Dense retrieval is a key component in systems like RAG and semantic search, providing superior accuracy in understanding user intent and delivering relevant results.
Frequently Asked Questions
What are the advantages of dense retrieval over sparse retrieval?
Dense retrieval captures semantic relationships between words, making it ideal for tasks like contextual retrieval.
What tools or models are commonly used for dense retrieval?
Popular tools include vector databases, powered by models like BERT and RoBERTa.
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