标题： $\text{E}^2\text{Rank}$: 你的文本嵌入也可以是一个有效且高效的列表重新排序器 显示英文标题

标题： $\text{E}^2\text{Rank}$: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker

摘要： 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，它们以高效率提供了具有竞争力的检索性能。 然而，与专门的重排序器相比，尤其是最近基于大语言模型的列表重排序器，它们的排序保真度仍然有限，这些重排序器能够捕捉细粒度的查询-文档和文档-文档交互。 在本文中，我们提出了一种简单而有效的统一框架$\text{E}^2\text{Rank}$，即高效嵌入式排序（也称为嵌入到排序），该框架通过在列表排序目标下的持续训练，将单个文本嵌入模型扩展为同时执行高质量检索和列表重排序，从而实现了强大的效果和显著的效率。 通过将查询和文档嵌入之间的余弦相似性作为统一的排序函数，从原始查询及其候选文档构建的列表排序提示，作为一种经过顶部K文档信号增强的增强查询，类似于传统检索模型中的伪相关反馈（PRF）。 这种设计保留了基础嵌入模型的效率和表征质量，同时显著提高了其重排序性能。 实证研究表明，$\textrm{E}^2\text{Rank}$在BEIR重排序基准上取得了最先进的结果，并在计算密集型BRIGHT基准上表现出具有竞争力的性能，且重排序延迟非常低。 我们还表明，重排序训练过程提升了MTEB基准上的嵌入性能。 我们的研究结果表明，单一的嵌入模型可以有效地统一检索和重排序，提供计算效率和具有竞争力的排序准确性。 显示英文摘要

摘要： 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 $\text{E}^2\text{Rank}$, 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, $\textrm{E}^2\text{Rank}$ 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.