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E2Rank unifies text embedding and reranking for efficient search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed E2Rank, a novel framework that unifies text embedding and listwise reranking for more effective and efficient search. This approach extends a single text embedding model to perform both retrieval and reranking by treating the reranking prompt as a pseudo-relevance feedback query. E2Rank achieves state-of-the-art results on the BEIR benchmark, competitive performance on BRIGHT, and significantly lower latency than existing LLM-based rerankers, while also improving embedding performance on MTEB. AI

IMPACT This research could lead to more efficient and accurate search systems by combining embedding and reranking techniques.

RANK_REASON The cluster contains an academic paper detailing a new method for search, which falls under the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

E2Rank unifies text embedding and reranking for efficient search

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The cluster contains an academic paper detailing a new method for search, which falls under the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Qi Liu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Jiaxin Mao ·

    E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search

    arXiv:2510.22733v3 Announce Type: replace 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-do…

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