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

arXiv: https://arxiv.org/abs/2406.11678 · HTML: https://mlsanity.com/paper/2406.11678/

## 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 (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Improving Passage Retrieval with Zero-Shot Question Generation](https://mlsanity.com/paper/2204.07496/) (2022)
- [RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://mlsanity.com/paper/2312.02724/) (2023)
- [RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses](https://mlsanity.com/paper/2210.10634/) (2022)
- [From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective](https://mlsanity.com/paper/2205.04733/) (2022)
- [Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels](https://mlsanity.com/paper/2310.14122/) (2023)
- [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://mlsanity.com/paper/2101.05667/) (2021)
- [ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval](https://mlsanity.com/paper/2402.15838/) (2024)
- [Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers](https://mlsanity.com/paper/2311.01555/) (2023)
- [Pre-training Methods in Information Retrieval](https://mlsanity.com/paper/2111.13853/) (2021)
- [MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers](https://mlsanity.com/paper/2404.11960/) (2024)

## Built on by (most cited first, 30 of 47)

- [Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning](https://mlsanity.com/paper/2503.06034/) (2025)
- [Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers](https://mlsanity.com/paper/2410.02642/) (2024)
- [Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2501.15228/) (2025)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models](https://mlsanity.com/paper/2406.14848/) (2024)
- [Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2509.03817/) (2025)
- [Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge](https://mlsanity.com/paper/2602.09341/) (2026)
- [DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task](https://mlsanity.com/paper/2406.16332/) (2024)
- [TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks](https://mlsanity.com/paper/2602.13272/) (2026)
- [FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering](https://mlsanity.com/paper/2601.06992/) (2026)
- [LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking](https://mlsanity.com/paper/2504.07439/) (2025)
- [CoRanking: Collaborative Ranking with Small and Large Ranking Agents](https://mlsanity.com/paper/2503.23427/) (2025)
- [Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning](https://mlsanity.com/paper/2603.07972/) (2026)
- [AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking](https://mlsanity.com/paper/2505.18512/) (2025)
- [OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries](https://mlsanity.com/paper/2605.06235/) (2026)
- [How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective](https://mlsanity.com/paper/2504.07898/) (2025)
- [MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.01005/) (2025)
- [Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information](https://mlsanity.com/paper/2506.10859/) (2025)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [RaMem: Contextual Reinstatement for Long-term Agentic Memory](https://mlsanity.com/paper/2606.22844/) (2026)
- [How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models](https://mlsanity.com/paper/2508.16757/) (2025)
- [E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search](https://mlsanity.com/paper/2510.22733/) (2025)
- [Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scales](https://mlsanity.com/paper/2505.19334/) (2025)
- [Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers](https://mlsanity.com/paper/2507.06223/) (2025)
- [V-Agent: An Interactive Video Search System Using Vision-Language Models](https://mlsanity.com/paper/2512.16925/) (2025)
- [DiffuRank: Effective Document Reranking with Diffusion Language Models](https://mlsanity.com/paper/2602.12528/) (2026)
- [PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debate](https://mlsanity.com/paper/2505.17492/) (2025)
- [LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2602.13571/) (2026)

## 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.