Verified80% confidenceFactTime unknown
The reranking stage typically uses a Cross-Encoder, which jointly encodes the query and document by concatenating them, enabling more accurate relevance judgment than bi-encoder models but at a higher computational cost.
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6/1/2026
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VerifiedA Cross-Encoder concatenates a query with each candidate document and feeds them jointly into the same model, capturing word-level interactions through the attention mechanism; its ranking precision is superior to vector similarity, but latency grows linearly with the size of the candidate set.84% similarVerifiedRerankers are typically implemented using cross-encoders, which concatenate the query and document and feed them jointly into a Transformer model, achieving significantly higher ranking precision than the initial retrieval stage80% similarUnverifiedA cross-encoder concatenates the query and document into a single sequence for joint encoding via full attention mechanisms, offering high accuracy but with O(n) computational complexity, making it suitable only for re-ranking the top-K results (typically 20–100 items) from initial retrieval.76% similarVerifiedCross-encoders are more accurate than bi-encoders because they concatenate [query, doc] and feed it into a Transformer, letting the attention mechanism compare directly at the word level, but they cannot pre-build an index and can only be used for fine ranking.75% similar
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