Improve dataset card for FutureQueryEval: Add paper, code, project links, update metadata and content

README.md

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----
-license: apache-2.0
----
+---
+license: mit
+task_categories:
+- text-ranking
+language:
+- en
+tags:
+- information-retrieval
+- reranking
+- llm
+- benchmark
+- temporal
+- llm-reranking
+---
+
+# How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models 🔍
+
+This repository contains the **FutureQueryEval Dataset** presented in the paper [How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models](https://huggingface.co/papers/2508.16757).
+
+Code: [https://github.com/DataScienceUIBK/llm-reranking-generalization-study](https://github.com/DataScienceUIBK/llm-reranking-generalization-study)
+
+Project Page / Leaderboard: [https://rankarena.ngrok.io](https://rankarena.ngrok.io)
+
+## 🎉 News
+- **[2025-08-22]** 🎯 **FutureQueryEval Dataset Released!** - The first temporal IR benchmark with queries from April 2025+
+- **[2025-08-22]** 🔧 Comprehensive evaluation framework released - 22 reranking methods, 40 variants tested
+- **[2025-08-22]** 📊 Integrated with [RankArena](https://arxiv.org/abs/2508.05512) leaderboard. You can view and interact with RankArena through this [link](https://rankarena.ngrok.io)
+- **[2025-08-20]** 📝 Paper accepted at EMNLP Findings 2025
+
+## 📖 Introduction
+
+We present the **most comprehensive empirical study of reranking methods** to date, systematically evaluating 22 state-of-the-art approaches across 40 variants. Our key contribution is **FutureQueryEval** - the first temporal benchmark designed to test reranker generalization on truly novel queries unseen during LLM pretraining.
+
+<div align="center">
+<img src="https://github.com/DataScienceUIBK/llm-reranking-generalization-study/blob/main/figures/radar.jpg" alt="Performance Overview" width="600"/>
+<p><em>Performance comparison across pointwise, pairwise, and listwise reranking paradigms</em></p>
+</div>
+
+### Key Findings 🔍
+- **Temporal Performance Gap**: 5-15% performance drop on novel queries compared to standard benchmarks
+- **Listwise Superiority**: Best generalization to unseen content (8% avg. degradation vs 12-15% for others)
+- **Efficiency Trade-offs**: Comprehensive runtime analysis reveals optimal speed-accuracy combinations
+- **Domain Vulnerabilities**: All methods struggle with argumentative and informal content
+
+# 📄 FutureQueryEval Dataset
+
+## Overview
+**FutureQueryEval** is a novel IR benchmark comprising **148 queries** with **2,938 query-document pairs** across **7 topical categories**, designed to evaluate reranker performance on temporal novelty.
+
+### 🎯 Why FutureQueryEval?
+- **Zero Contamination**: All queries refer to events after April 2025
+- **Human Annotated**: 4 expert annotators with quality control
+- **Diverse Domains**: Technology, Sports, Politics, Science, Health, Business, Entertainment
+- **Real Events**: Based on actual news and developments, not synthetic data
+
+### 📊 Dataset Statistics
+| Metric | Value |
+|--------|-------|
+| Total Queries | 148 |
+| Total Documents | 2,787 |
+| Query-Document Pairs | 2,938 |
+| Avg. Relevant Docs per Query | 6.54 |
+| Languages | English |
+| License | MIT |
+
+### 🌍 Category Distribution
+- **Technology**: 25.0% (37 queries)
+- **Sports**: 20.9% (31 queries)  
+- **Science & Environment**: 13.5% (20 queries)
+- **Business & Finance**: 12.8% (19 queries)
+- **Health & Medicine**: 10.8% (16 queries)
+- **World News & Politics**: 9.5% (14 queries)
+- **Entertainment & Culture**: 7.4% (11 queries)
+
+### 📝 Example Queries
+```
+🌍 World News & Politics:
+"What specific actions has Egypt taken to support injured Palestinians from Gaza, 
+as highlighted during the visit of Presidents El-Sisi and Macron to Al-Arish General Hospital?"
+
+⚽ Sports:
+"Which teams qualified for the 2025 UEFA European Championship playoffs in June 2025?"
+
+💻 Technology:
+"What are the key features of Apple's new Vision Pro 2 announced at WWDC 2025?"
+```
+
+## Data Collection Methodology
+1. **Source Selection**: Major news outlets, official sites, sports organizations
+2. **Temporal Filtering**: Events after April 2025 only
+3. **Query Creation**: Manual generation by domain experts
+4. **Novelty Validation**: Tested against GPT-4 knowledge cutoff
+5. **Quality Control**: Multi-annotator review with senior oversight
+
+# 📊 Evaluation Results
+
+## Top Performers on FutureQueryEval
+
+| Method Category | Best Model | NDCG@10 | Runtime (s) |
+|----------------|------------|---------|-------------|
+| **Listwise** | Zephyr-7B | **62.65** | 1,240 |
+| **Pointwise** | MonoT5-3B | **60.75** | 486 |
+| **Setwise** | Flan-T5-XL | **56.57** | 892 |
+| **Pairwise** | EchoRank-XL | **54.97** | 2,158 |
+| **Tournament** | TourRank-GPT4o | **62.02** | 3,420 |
+
+## Performance Insights
+- 🏆 **Best Overall**: Zephyr-7B (62.65 NDCG@10)
+- ⚡ **Best Efficiency**: FlashRank-MiniLM (55.43 NDCG@10, 195s)
+- 🎯 **Best Balance**: MonoT5-3B (60.75 NDCG@10, 486s)
+
+<div align="center">
+<img src="https://github.com/DataScienceUIBK/llm-reranking-generalization-study/blob/main/figures/efficiency_tradeoff.png.jpg" alt="Efficiency Analysis" width="700"/>
+<p><em>Runtime vs. Performance trade-offs across reranking methods</em></p>
+</div>
+
+# 🔧 Supported Methods
+
+We evaluate **22 reranking approaches** across multiple paradigms:
+
+### Pointwise Methods
+- MonoT5, RankT5, InRanker, TWOLAR
+- FlashRank, Transformer Rankers
+- UPR, MonoBERT, ColBERT
+
+### Listwise Methods  
+- RankGPT, ListT5, Zephyr, Vicuna
+- LiT5-Distill, InContext Rerankers
+
+### Pairwise Methods
+- PRP (Pairwise Ranking Prompting)
+- EchoRank
+
+### Advanced Methods
+- Setwise (Flan-T5 variants)
+- TourRank (Tournament-based)
+- RankLLaMA (Task-specific fine-tuned)
+
+# 🔄 Dataset Updates
+
+**FutureQueryEval will be updated every 6 months** with new queries about recent events to maintain temporal novelty. Subscribe to releases for notifications!
+
+## Upcoming Updates
+- **Version 1.1** (December 2025): +100 queries from July-September 2025 events
+- **Version 1.2** (June 2026): +100 queries from October 2025-March 2026 events
+
+# 📋 Leaderboard
+
+Submit your reranking method results to appear on our leaderboard! See [SUBMISSION.md](https://github.com/DataScienceUIBK/llm-reranking-generalization-study/blob/main/SUBMISSION.md) for guidelines.
+
+Current standings available at: [RanArena](https://rankarena.ngrok.io) 
+
+# 🤝 Contributing
+
+We welcome contributions! See [CONTRIBUTING.md](https://github.com/DataScienceUIBK/llm-reranking-generalization-study/blob/main/CONTRIBUTING.md) for:
+- Adding new reranking methods
+- Improving evaluation metrics  
+- Dataset quality improvements
+- Bug fixes and optimizations
+
+# 🎈 Citation
+
+If you use FutureQueryEval or our evaluation framework, please cite:
+
+```bibtex
+@misc{abdallah2025howgoodarellmbasedrerankers,
+      title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models},
+      author={Abdelrahman Abdallah and Bhawna Piryani},
+      year={2025},
+      eprint={2508.16757},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```
+
+# 📞 Contact
+
+- **Authors**: [Abdelrahman Abdallah](mailto:[email protected]), [Bhawna Piryani](mailto:[email protected])
+- **Institution**: University of Innsbruck
+- **Issues**: Please use GitHub Issues for bug reports and feature requests
+
+---
+
+<div align="center">
+<p>⭐ Star this repo if you find it helpful! ⭐</p>
+<p>📧 Questions? Open an issue or contact the authors</p>
+</div>