Unverified50% confidenceFactExact time
A cross-encoder concatenates the query and document and feeds them together into a Transformer, which can capture deeper relevance signals, but document representations cannot be precomputed, and each query-document pair requires one forward pass, so it is only suitable for reranking a small number of candidates.
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10/18/2026
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VerifiedRerankers 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% 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.78% similarVerifiedA 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.74% similarVerifiedThe 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.72% similarVerifiedThe Transformer's encoder is responsible for mapping the input sequence to a contextual representation and is suitable for classification and understanding tasks, while the decoder autoregressively generates the output sequence and is suitable for text generation tasks72% similar
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