E^2Rank：你的文本嵌入模型亦可成為高效列表式重排器

E^2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker

October 26, 2025

作者: Qi Liu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Jiaxin Mao

cs.AI

摘要

文本嵌入模型在现实搜索应用中扮演着基础性角色。通过将查询和文档映射到共享的嵌入空间，这类模型能以高效率实现具有竞争力的检索性能。然而与专用重排器相比，其排序保真度仍存在局限，特别是相较于近期基于大语言模型的列表级重排器——后者能捕捉细粒度的查询-文档及文档-文档交互关系。本文提出名为E^2Rank的简洁高效统一框架（意为"基于嵌入的高效排序"，亦指"嵌入到排序"），通过列表级排序目标下的持续训练，将单一文本嵌入模型扩展至同时执行高质量检索与列表级重排，从而在保持卓越效率的同时实现强劲效能。该框架以查询嵌入与文档嵌入的余弦相似度作为统一排序函数，而由原始查询及其候选文档构建的列表级排序提示，则充当了融入Top-K文档信号的增强型查询，类似于传统检索模型中的伪相关性反馈。这一设计在保留基础嵌入模型效率与表征质量的同时，显著提升了其重排性能。实验表明，E^2Rank在BEIR重排基准测试中达到最先进水平，在需要深度推理的BRIGHT基准测试中展现出竞争优势，且重排延迟极低。我们还发现排序训练过程能提升模型在MTEB基准测试中的嵌入性能。研究结果表明，单一嵌入模型可有效统一检索与重排任务，兼具计算效率与竞争优势的排序准确性。

English

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 E^2Rank, 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, E^2Rank 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.