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.

翻译：文本嵌入模型是现实世界搜索应用中的基础组件。通过将查询和文档映射到共享的嵌入空间，它们能以高效率提供具有竞争力的检索性能。然而，与专用的重排序器（尤其是近期基于大语言模型的列表式重排序器）相比，其排序保真度仍然有限，后者能够捕捉细粒度的查询-文档及文档-文档交互。本文提出了一种简单而有效的统一框架E2Rank（意为高效基于嵌入的排序，亦指嵌入到排序），该框架通过在一个列表式排序目标下进行持续训练，将单一文本嵌入模型扩展至同时执行高质量检索和列表式重排序，从而在保持卓越效率的同时实现强大的有效性。通过使用查询与文档嵌入之间的余弦相似度作为统一的排序函数，由原始查询及其候选文档构建的列表式排序提示，可充当一个增强了来自前K个文档信号的查询（类似于传统检索模型中的伪相关反馈）。这一设计在保持基础嵌入模型效率与表征质量的同时，显著提升了其重排序性能。实证结果表明，E2Rank在BEIR重排序基准测试中取得了最先进的结果，并在推理密集型的BRIGHT基准测试中展现出有竞争力的性能，且重排序延迟极低。我们还发现，排序训练过程提升了模型在MTEB基准测试上的嵌入性能。我们的研究结果表明，单一嵌入模型能够有效统一检索与重排序，在提供计算效率的同时，也具备有竞争力的排序准确性。