mlsanityData to 7 Oct 2026

Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models

arXiv 2406.14848 ↗First posted 21 Jun 2024cs.CL cs.IRcited by 20

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Abstract (arXiv)

Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new state-of-the-art in this task. However, the efficiency of RankGPT models is limited by the maximum context length and relatively high latency of LLM inference. To address these issues, in this paper, we propose PE-Rank, leveraging the single passage embedding as a good context compression for efficient listwise passage reranking. By treating each passage as a special token, we can directly input passage embeddings into LLMs, thereby reducing input length. Additionally, we introduce an inference method that dynamically constrains the decoding space to these special tokens, accelerating the decoding process. For adapting the model to reranking, we employ listwise learning to rank loss for training. Evaluation results on multiple benchmarks demonstrate that PE-Rank significantly improves efficiency in both prefilling and decoding, while maintaining competitive ranking effectiveness. The Code is available at https://github.com/liuqi6777/pe_rank.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

33 of 52 references.

References · 33

  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. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,611
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  6. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  7. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  8. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  9. 2019Passage Re-ranking with BERTcited by 629
  10. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  11. 2023Learning to Compress Prompts with Gist Tokenscited by 379
  12. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
21 more
  1. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
  2. 2023Adapting Language Models to Compress Contextscited by 350
  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. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 279
  6. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 271
  7. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  8. 2019Multi-Stage Document Ranking with BERTcited by 200
  9. 2023Text Embeddings Reveal (Almost) As Much As Textcited by 189
  10. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 164
  11. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  12. 2021Optimizing Dense Retrieval Model Training with Hard Negativescited by 154
  13. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
  14. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 123
  15. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  16. 2023Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documentscited by 103
  17. 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
  18. 2024TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategycited by 47
  19. 2020RepBERT: Contextualized Text Embeddings for First-Stage Retrievalcited by 46
  20. 2024ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrievalcited by 29
  21. 2024Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddingscited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 11; 2026: 5.

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  1. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  2. 2025jina-reranker-v3: Last but Not Late Interaction for Listwise Document Rerankingcited by 23
  3. 2024DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Taskcited by 17
  4. 2024ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillationcited by 9
  5. 2025In-context Ranking Preference Optimizationcited by 9
  6. 2026Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inferencecited by 8
  7. 2025How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspectivecited by 6
  8. 2025The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Paperscited by 6
  9. 2025Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Rerankingcited by 6
  10. 2025CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrievalcited by 5
  11. 2025+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checkingcited by 2
  12. 2025Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generationcited by 1
8 more
  1. 2025What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Modelscited by 1
  2. 2024RaCT: Ranking-aware Chain-of-Thought Optimization for LLMscited by 0
  3. 2025Embedding-Based Context-Aware Rerankercited by 0
  4. 2025Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddingscited by 0
  5. 2026ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compressioncited by 0
  6. 2026CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoringcited by 0
  7. 2026From Model Patterns to Abstract Semantics in Compositional Zero-Shot Learningcited by 0
  8. 2026RankBuffer: Efficient Ranking-Based Rewards for Open-Ended Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Rank-K: Test-Time Reasoning for Listwise Reranking
  2. 2025GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs
  3. 2025ListConRanker: A Contrastive Text Reranker with Listwise Encoding
  4. 2025ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking
  5. 2026Efficient Listwise Reranking with Compressed Document Representations
  6. 2025Language Ranker: A Lightweight Ranking framework for LLM Decoding
  7. 2025ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
  8. 2026LRanker: LLM Ranker for Massive Candidates ↗