E^2Rank: Your Text Embedding can Also be an Effective
  and Efficient Listwise Reranker
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.
6900329b22d452aac6dd431c
Qi Liu
claimed_verified
2025-10-28T15:36:34.058Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/630b167317bbe470568db9b5/WUFw0-EmPBbN5o62HEZsV.png
Qi Liu
liuqi6777
user
liuqi6777
6900329b22d452aac6dd431d
Yanzhao Zhang
6900329b22d452aac6dd431e
Mingxin Li
6900329b22d452aac6dd431f
Dingkun Long
6900329b22d452aac6dd4320
Pengjun Xie
6900329b22d452aac6dd4321
Jiaxin Mao
claimed_verified
2026-01-28T11:33:24.675Z
6979872d62e9590654007360
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/1yBdsuY20QOhc5hcysefc.png
Jiaxin Mao
defaultstr
user
defaultstr
E^2Rank: Your Text Embedding can Also be an Effective
  and Efficient Listwise Reranker
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.
A unified framework extends a single text embedding model to perform both retrieval and listwise reranking, achieving state-of-the-art results with low latency.
text embedding models shared embedding space ranking fidelity LLM-based listwise rerankers listwise ranking objective cosine similarity unified ranking function listwise ranking prompt pseudo-relevance feedback BEIR reranking benchmark BRIGHT benchmark MTEB benchmark embedding performance