E2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker

• URL: http://arxiv.org/abs/2510.22733v2
• Date: Fri, 31 Oct 2025 03:47:20 GMT
• Title: E2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker
• Authors: Qi Liu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Jiaxin Mao,
• Abstract summary: We propose a simple yet effective unified framework E2Rank, which extends a single text embedding model to perform both high-quality retrieval and listwise reranking.<n>By applying cosine similarity between the query and document embeddings as a unified ranking function, the listwise ranking prompt serves as an enhanced query enriched with signals from the top-K documents.<n>E2Rank achieves state-of-the-art results on the reasoning-intensive BRIGHT benchmark, with very low reranking latency.
• Score: 39.61739668491574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text embedding models serve as a fundamental component in real-world search applications. By mapping queries and documents into a shared embedding space, they deliver competitive retrieval performance with high efficiency. However, their ranking fidelity remains limited compared to dedicated rerankers, especially recent LLM-based listwise rerankers, which capture fine-grained query-document and document-document interactions. In this paper, we propose a simple yet effective unified framework E2Rank, means Efficient Embedding-based Ranking (also means Embedding-to-Rank), which extends a single text embedding model to perform both high-quality retrieval and listwise reranking through continued training under a listwise ranking objective, thereby achieving strong effectiveness with remarkable efficiency. By applying cosine similarity between the query and document embeddings as a unified ranking function, the listwise ranking prompt, which is constructed from the original query and its candidate documents, serves as an enhanced query enriched with signals from the top-K documents, akin to pseudo-relevance feedback (PRF) in traditional retrieval models. This design preserves the efficiency and representational quality of the base embedding model while significantly improving its reranking performance. Empirically, E2Rank achieves state-of-the-art results on the BEIR reranking benchmark and demonstrates competitive performance on the reasoning-intensive BRIGHT benchmark, with very low reranking latency. We also show that the ranking training process improves embedding performance on the MTEB benchmark. Our findings indicate that a single embedding model can effectively unify retrieval and reranking, offering both computational efficiency and competitive ranking accuracy.

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