E^2Rank: Your Text Embedding can Also be an Effective
  and Efficient Listwise Reranker
Authors: name: Qi Liu, name: Yanzhao Zhang, name: Mingxin Li, name: Dingkun Long, name: Pengjun Xie, name: Jiaxin Mao
Organization: Alibaba-NLP

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.