mlsanityData to 7 Oct 2026

RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

arXiv 2312.02724 ↗First posted 5 Dec 2023cs.IRcited by 151

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Cited by 41 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In information retrieval, proprietary large language models (LLMs) such as GPT-4 and open-source counterparts such as LLaMA and Vicuna have played a vital role in reranking. However, the gap between open-source and closed models persists, with reliance on proprietary, non-transparent models constraining reproducibility. Addressing this gap, we introduce RankZephyr, a state-of-the-art, open-source LLM for listwise zero-shot reranking. RankZephyr not only bridges the effectiveness gap with GPT-4 but in some cases surpasses the proprietary model. Our comprehensive evaluations across several datasets (TREC Deep Learning Tracks; NEWS and COVID from BEIR) showcase this ability. RankZephyr benefits from strategic training choices and is resilient against variations in initial document ordering and the number of documents reranked. Additionally, our model outperforms GPT-4 on the NovelEval test set, comprising queries and passages past its training period, which addresses concerns about data contamination. To foster further research in this rapidly evolving field, we provide all code necessary to reproduce our results at https://github.com/castorini/rank_llm.

Key facts. Cited by 151 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Dec 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 35; 2025: 55; 2026: 60.

151 papers.

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  1. 2024Generative Representational Instruction Tuningcited by 227
  2. 2025Rank1: Test-Time Compute for Reranking in Information Retrievalcited by 68
  3. 2024FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructionscited by 67
  4. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
  5. 2025Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learningcited by 58
  6. 2024TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategycited by 47
  7. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 42
  8. 2024CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Rerankingcited by 37
  9. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  10. 2024RAR-b: Reasoning as Retrieval Benchmarkcited by 33
  11. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Trackcited by 31
  12. 2025Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-rankingcited by 30
38 more
  1. 2024Semantic Operators: A Declarative Model for Rich, AI-based Data Processingcited by 29
  2. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Rerankingcited by 28
  3. 2024Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Frameworkcited by 28
  4. 2025Rank-K: Test-Time Reasoning for Listwise Rerankingcited by 28
  5. 2025REARANK: Reasoning Re-ranking Agent via Reinforcement Learningcited by 24
  6. 2024Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Modelscited by 20
  7. 2025SweRank: Software Issue Localization with Code Rankingcited by 20
  8. 2024Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogscited by 18
  9. 2024Query Performance Prediction using Relevance Judgments Generated by Large Language Modelscited by 18
  10. 2024Characterizing Prompt Compression Methods for Long Context Inferencecited by 18
  11. 2024Can We Use Large Language Models to Fill Relevance Judgment Holes?cited by 17
  12. 2025Guiding Retrieval using LLM-based Listwise Rankerscited by 16
  13. 2025Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generationcited by 13
  14. 2025Shifting from Ranking to Set Selection for Retrieval Augmented Generationcited by 13
  15. 2024LitLLMs, LLMs for Literature Review: Are we there yet?cited by 12
  16. 2025LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Rerankingcited by 12
  17. 2025ThinkQE: Query Expansion via an Evolving Thinking Processcited by 11
  18. 2025DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillationcited by 11
  19. 2025CoRanking: Collaborative Ranking with Small and Large Ranking Agentscited by 10
  20. 2024ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillationcited by 9
  21. 2025Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Modelscited by 9
  22. 2025CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoningcited by 9
  23. 2023Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languagescited by 8
  24. 2024rerankers: A Lightweight Python Library to Unify Ranking Methodscited by 8
  25. 2024Drowning in Documents: Consequences of Scaling Reranker Inferencecited by 8
  26. 2025HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reusecited by 8
  27. 2025Batched Self-Consistency Improves LLM Relevance Assessment and Rankingcited by 8
  28. 2025AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Rerankingcited by 8
  29. 2025Scalable In-context Ranking with Generative Modelscited by 8
  30. 2024ReSLLM: Large Language Models are Strong Resource Selectors for Federated Searchcited by 7
  31. 2024MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankerscited by 7
  32. 2024Self-Calibrated Listwise Reranking with Large Language Modelscited by 7
  33. 2025Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrievalcited by 7
  34. 2025Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Shortcited by 7
  35. 2025PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answeringcited by 7
  36. 2024Squid: Long Context as a New Modality for Energy-Efficient On-Device Language Modelscited by 6
  37. 2024HyQE: Ranking Contexts with Hypothetical Query Embeddingscited by 6
  38. 2024RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Enginecited by 6

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