mlsanityData to 7 Oct 2026

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

arXiv 2510.22733 ↗First posted 26 Oct 2025cs.CL cs.AI cs.IRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 41 of its 58 references that are in the map. First posted on arXiv 26 Oct 2025.

Builds on

41 of 58 references.

References · 41

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  7. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  8. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  9. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  10. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  11. 2024LLM2Vec: Large Language Models Are Secretly Powerful Text Encoderscited by 396
  12. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
29 more
  1. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  2. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 329
  3. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 302
  4. 2020Overview of the TREC 2019 deep learning trackcited by 282
  5. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 271
  6. 2024Generative Representational Instruction Tuningcited by 227
  7. 2019Multi-Stage Document Ranking with BERTcited by 200
  8. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 164
  9. 2024BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalcited by 153
  10. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
  11. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  12. 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
  13. 2024Making Text Embedders Few-Shot Learnerscited by 78
  14. 2025Rank1: Test-Time Compute for Reranking in Information Retrievalcited by 68
  15. 2024FIRST: Faster Improved Listwise Reranking with Single Token Decodingcited by 54
  16. 2024TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategycited by 47
  17. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedbackcited by 45
  18. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 42
  19. 2025ReasonRank: Empowering Passage Ranking with Strong Reasoning Abilitycited by 39
  20. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  21. 2025Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-rankingcited by 30
  22. 2024ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrievalcited by 29
  23. 2021Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfallscited by 26
  24. 2023When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasetscited by 25
  25. 2024DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Taskcited by 17
  26. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Studycited by 13
  27. 2023Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?cited by 12
  28. 2025LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Rerankingcited by 12
  29. 2025ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Rerankingcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluationcited by 3
  2. 2026DiffuRank: Effective Document Reranking with Diffusion Language Modelscited by 3
  3. 2026ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compressioncited by 0