@inproceedings{liu-etal-2026-e2rank,
title = "{E}2{R}ank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search",
author = "Liu, Qi and
Zhang, Yanzhao and
Li, Mingxin and
Long, Dingkun and
Xie, Pengjun and
Mao, Jiaxin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2171/",
pages = "49363--49385",
ISBN = "979-8-89176-459-0",
abstract = "Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-based listwise rerankers, which capture fine-grained query-document and document-document interactions at high computational cost. We propose E2Rank (Efficient Embedding-based Ranking), a unified framework that extends a single text embedding model to perform both retrieval and listwise reranking via continued training under a listwise ranking objective. The key insight is to treat the listwise prompt{---}constructed from the query and its top-K candidates{---}as a pseudo-relevance feedback (PRF) query, enabling reranking via cosine similarity against precomputed document embeddings without autoregressive decoding. Empirically, E2Rank achieves state-of-the-art results on BEIR, competitive performance on the reasoning-intensive BRIGHT benchmark, significantly lower latency than existing LLM-based rerankers, and improved embedding performance on MTEB{---}all within a single model."
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%0 Conference Proceedings
%T E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search
%A Liu, Qi
%A Zhang, Yanzhao
%A Li, Mingxin
%A Long, Dingkun
%A Xie, Pengjun
%A Mao, Jiaxin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-e2rank
%X Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-based listwise rerankers, which capture fine-grained query-document and document-document interactions at high computational cost. We propose E2Rank (Efficient Embedding-based Ranking), a unified framework that extends a single text embedding model to perform both retrieval and listwise reranking via continued training under a listwise ranking objective. The key insight is to treat the listwise prompt—constructed from the query and its top-K candidates—as a pseudo-relevance feedback (PRF) query, enabling reranking via cosine similarity against precomputed document embeddings without autoregressive decoding. Empirically, E2Rank achieves state-of-the-art results on BEIR, competitive performance on the reasoning-intensive BRIGHT benchmark, significantly lower latency than existing LLM-based rerankers, and improved embedding performance on MTEB—all within a single model.
%U https://aclanthology.org/2026.emnlp-main.2171/
%P 49363-49385
Markdown (Informal)
[E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search](https://aclanthology.org/2026.emnlp-main.2171/) (Liu et al., EMNLP 2026)
ACL