mlsanityData to 14 Oct 2026

TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy

arXiv 2406.11678 ↗First posted 17 Jun 2024cs.IR cs.CLcited by 47

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Abstract (arXiv)

Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precluding them from processing a large number of documents simultaneously; (2) The output document sequence is influenced by the input order of documents, resulting in inconsistent ranking outcomes; (3) Achieving a balance between cost and ranking performance is challenging. To tackle these issues, we introduce a novel documents ranking method called TourRank, which is inspired by the sport tournaments, such as FIFA World Cup. Specifically, we 1) overcome the limitation in input length and reduce the ranking latency by incorporating a multi-stage grouping strategy similar to the parallel group stage of sport tournaments; 2) improve the ranking performance and robustness to input orders by using a points system to ensemble multiple ranking results. We test TourRank with different LLMs on the TREC DL datasets and the BEIR benchmark. The experimental results demonstrate that TourRank delivers state-of-the-art performance at a modest cost. The code of TourRank can be seen on https://github.com/chenyiqun/TourRank.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

24 of 41 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  7. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  8. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  9. 2019Passage Re-ranking with BERTcited by 645
  10. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
  11. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 360
  12. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
12 more
  1. 2020Overview of the TREC 2019 deep learning trackcited by 286
  2. 2021Overview of the TREC 2020 deep learning trackcited by 196
  3. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 165
  4. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 155
  5. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 115
  6. 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
  7. 2023Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labelscited by 86
  8. 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
  9. 2024ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrievalcited by 30
  10. 2023Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankerscited by 19
  11. 2021Pre-training Methods in Information Retrievalcited by 8
  12. 2024MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankerscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 19; 2026: 25.

47 papers.

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  1. 2025Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learningcited by 60
  2. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 43
  3. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learningcited by 39
  4. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  5. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  6. 2024Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Modelscited by 20
  7. 2025Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learningcited by 18
  8. 2026Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judgecited by 18
  9. 2024DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Taskcited by 17
  10. 2026TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Taskscited by 17
  11. 2026FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answeringcited by 16
  12. 2025LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Rerankingcited by 12
35 more
  1. 2025CoRanking: Collaborative Ranking with Small and Large Ranking Agentscited by 10
  2. 2026Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learningcited by 9
  3. 2025AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Rerankingcited by 8
  4. 2026OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queriescited by 8
  5. 2025How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspectivecited by 7
  6. 2025MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generationcited by 7
  7. 2025Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Informationcited by 6
  8. 2025Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendationcited by 6
  9. 2026LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptationcited by 6
  10. 2026RaMem: Contextual Reinstatement for Long-term Agentic Memorycited by 6
  11. 2025How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Modelscited by 4
  12. 2025E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Searchcited by 4
  13. 2025Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scalescited by 3
  14. 2025Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankerscited by 3
  15. 2025V-Agent: An Interactive Video Search System Using Vision-Language Modelscited by 3
  16. 2026DiffuRank: Effective Document Reranking with Diffusion Language Modelscited by 3
  17. 2025PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debatecited by 2
  18. 2026LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systemscited by 2
  19. 2026MemRerank: Preference Memory for Personalized Product Rerankingcited by 2
  20. 2025JointRank: Rank Large Set with Single Passcited by 1
  21. 2026BlitzRank: Principled Zero-shot Ranking Agents with Tournament Graphscited by 0
  22. 2026Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgmentscited by 0
  23. 2026VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Modelscited by 0
  24. 2026BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Eliminationcited by 0
  25. 2026ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compressioncited by 0
  26. 2026TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendationcited by 0
  27. 2026Active Learners as Efficient PRP Rerankerscited by 0
  28. 2026DualView: Adaptive Local-Global Fusion for Multi-Hop Document Rerankingcited by 0
  29. 2026Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0
  30. 2026Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorerscited by 0
  31. 2026Replacing Training with Memory: Listwise Selection for Text-to-SQLcited by 0
  32. 2026PEAR: Progressive Evidence-Based AutoResearch for Industrial Search Systemscited by 0
  33. 2026RankBuffer: Efficient Ranking-Based Rewards for Open-Ended Generationcited by 0
  34. 2026Component-Aware Feedback for Self-Evolving Programscited by 0
  35. 2026RPTune: Learned Context Curation for LLM Catalog Searchcited by 0