What is a Cross-Encoder?
A type of encoder that jointly processes query-document pairs to determine relevance.
More about Cross-Encoder:
Cross-Encoders take a query and a document as input and process them together through a single model to calculate relevance. Unlike bi-encoders, which generate embeddings independently, cross-encoders allow for finer-grained relevance scoring by considering the interaction between query and document.
Cross-encoders are often used in tasks requiring high accuracy, such as re-ranking candidates retrieved by dense retrieval or hybrid search.
Frequently Asked Questions
What are the advantages of cross-encoders?
They provide higher relevance accuracy by analyzing query-document interactions directly.
When should cross-encoders be used over bi-encoders?
Cross-encoders are better for re-ranking results, while bi-encoders are more efficient for large-scale retrieval.
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