mlsanityData to 7 Oct 2026

Zero-Shot Listwise Document Reranking with a Large Language Model

arXiv 2305.02156 ↗First posted 3 May 2023cs.IR cs.CLcited by 81

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Abstract (arXiv)

Supervised ranking methods based on bi-encoder or cross-encoder architectures have shown success in multi-stage text ranking tasks, but they require large amounts of relevance judgments as training data. In this work, we propose Listwise Reranker with a Large Language Model (LRL), which achieves strong reranking effectiveness without using any task-specific training data. Different from the existing pointwise ranking methods, where documents are scored independently and ranked according to the scores, LRL directly generates a reordered list of document identifiers given the candidate documents. Experiments on three TREC web search datasets demonstrate that LRL not only outperforms zero-shot pointwise methods when reranking first-stage retrieval results, but can also act as a final-stage reranker to improve the top-ranked results of a pointwise method for improved efficiency. Additionally, we apply our approach to subsets of MIRACL, a recent multilingual retrieval dataset, with results showing its potential to generalize across different languages.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). Builds on 25 of its 30 references that are in the map. First posted on arXiv 3 May 2023.

Builds on

25 of 30 references.

References · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  6. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  7. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  8. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  9. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  10. 2019Passage Re-ranking with BERTcited by 629
  11. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  12. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
13 more
  1. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  2. 2020Overview of the TREC 2019 deep learning trackcited by 282
  3. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  4. 2019Multi-Stage Document Ranking with BERTcited by 200
  5. 2021Overview of the TREC 2020 deep learning trackcited by 193
  6. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 164
  7. 2021mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datasetcited by 116
  8. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  9. 2021Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipelinecited by 65
  10. 2022Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languagescited by 47
  11. 2023Language Models are Few-shot Learners for Prognostic Predictioncited by 37
  12. 2022Towards Best Practices for Training Multilingual Dense Retrieval Modelscited by 26
  13. 2023InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankerscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 13; 2024: 16; 2025: 31; 2026: 21.

81 papers.

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  1. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 302
  4. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  5. 2024Generative Representational Instruction Tuningcited by 227
  6. 2024MM-Embed: Universal Multimodal Retrieval with Multimodal LLMscited by 159
  7. 2023Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarizationcited by 70
  8. 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 69
  9. 2023A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Modelscited by 53
  10. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  11. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 42
  12. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systemscited by 38
38 more
  1. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  2. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  3. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Trackcited by 31
  4. 2023Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Modelscited by 30
  5. 2025Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-rankingcited by 30
  6. 2025Rank-K: Test-Time Reasoning for Listwise Rerankingcited by 28
  7. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  8. 2023ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrievalcited by 26
  9. 2025REARANK: Reasoning Re-ranking Agent via Reinforcement Learningcited by 24
  10. 2025MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrievalcited by 21
  11. 2023Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankerscited by 19
  12. 2025DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrieverscited by 19
  13. 2024LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Rankingcited by 16
  14. 2024METIS: Fast Quality-Aware RAG Systems with Configuration Adaptationcited by 16
  15. 2025Guiding Retrieval using LLM-based Listwise Rankerscited by 16
  16. 2023Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Modelscited by 14
  17. 2025DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generationcited by 14
  18. 2023Unsupervised Contrast-Consistent Ranking with Language Modelscited by 13
  19. 2026AgentIR: Reasoning-Aware Retrieval for Deep Research Agentscited by 13
  20. 2023Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Modelscited by 12
  21. 2024LitLLMs, LLMs for Literature Review: Are we there yet?cited by 12
  22. 2025Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBcited by 12
  23. 2025DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillationcited by 11
  24. 2025SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQLcited by 10
  25. 2025Batched Self-Consistency Improves LLM Relevance Assessment and Rankingcited by 8
  26. 2025AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Rerankingcited by 8
  27. 2025Scalable In-context Ranking with Generative Modelscited by 8
  28. 2024MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankerscited by 7
  29. 2024An Investigation of Prompt Variations for Zero-shot LLM-based Rankerscited by 7
  30. 2025Flex-Judge: Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluatorscited by 6
  31. 2025Influence Guided Context Selection for Effective Retrieval-Augmented Generationcited by 6
  32. 2023Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checkingcited by 5
  33. 2025syftr: Pareto-Optimal Generative AIcited by 5
  34. 2025TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Rankingcited by 5
  35. 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Modelcited by 4
  36. 2025Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrievalcited by 3
  37. 2025The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Modelscited by 3
  38. 2026Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generationcited by 3

50 most cited of 81 All 81 (JSON)