Verified75% confidenceFactTime unknown
Rerankers typically adopt a Cross-Encoder architecture, in contrast to the Bi-Encoder architecture used in vector retrieval.
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5/31/2026
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UnverifiedThe recall stage typically uses Bi-Encoder models (such as text-embedding-ada-002 or the BGE series), while the reranking stage uses Cross-Encoder models (such as Cohere Rerank or BGE-Reranker).75% 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.73% similarUnverified现代Sentence Embedding模型通常采用双塔（Bi-Encoder）结构，查询和文档分别独立编码为向量73% similarUnverifiedBi-Encoder将问题和文档分别独立编码成向量再计算相似度，Cross-Encoder将问题和候选文档拼接后一起输入模型通过注意力机制充分交互72% similarVerifiedA Bi-Encoder encodes queries and documents independently into vectors and computes relevance via cosine similarity or dot product; document vectors can be pre-computed offline and stored in a vector database.70% similar
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