What are Retrieval-based Models?
AI models that rely on retrieving relevant information, often using techniques like sparse retrieval or dense retrieval, rather than generating responses from scratch.
More about Retrieval-based Models:
Retrieval-based Models are AI systems designed to fetch and present the most relevant information from a predefined dataset or knowledge base. These models often utilize sparse retrieval methods for term-based matching or dense retrieval to capture semantic relationships.
Retrieval-based models are widely used in semantic search, question answering, and recommendation systems, where accuracy and relevance are crucial.
Frequently Asked Questions
What is the main advantage of retrieval-based models?
They provide highly accurate and fact-based responses by retrieving pre-existing information from reliable sources, such as vector databases.
How do retrieval-based models differ from generative models?
Retrieval-based models fetch existing data, while generative models create new text using approaches like RAG.
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