Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-retrieval
+language:
+- en
+tags:
+- information-retrieval
+- reranking
+- temporal-evaluation
+- benchmark
+size_categories:
+- 1K<n<10K
+---
+
+# FutureQueryEval Dataset 🔍
+
+## Dataset Description
+
+**FutureQueryEval** is a novel Information Retrieval (IR) benchmark designed to evaluate reranker performance on temporal novelty. It comprises **148 queries** with **2,938 query-document pairs** across **7 topical categories**, specifically created to test how well reranking models generalize to truly novel queries that were unseen during LLM pretraining.
+
+### Key Features
+
+- **Zero Contamination**: All queries refer to events after April 2025
+- **Human Annotated**: Created by 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
+- **Temporal Novelty**: First benchmark designed to test reranker generalization on post-training events
+
+## 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 | Apache-2.0 |
+
+## Category Distribution
+
+| Category | Queries | Percentage |
+|----------|---------|------------|
+| **Technology** | 37 | 25.0% |
+| **Sports** | 31 | 20.9% |
+| **Science & Environment** | 20 | 13.5% |
+| **Business & Finance** | 19 | 12.8% |
+| **Health & Medicine** | 16 | 10.8% |
+| **World News & Politics** | 14 | 9.5% |
+| **Entertainment & Culture** | 11 | 7.4% |
+
+## Dataset Structure
+
+The dataset consists of three main files:
+
+### Files
+
+- **`queries.tsv`**: Contains the query information
+  - Columns: `query_id`, `query_text`, `category`
+- **`corpus.tsv`**: Contains the document collection  
+  - Columns: `doc_id`, `title`, `text`, `url`
+- **`qrels.txt`**: Contains relevance judgments
+  - Format: `query_id 0 doc_id relevance_score`
+
+### Data Fields
+
+#### Queries
+- `query_id` (string): Unique identifier for each query
+- `query_text` (string): The natural language query
+- `category` (string): Topical category (Technology, Sports, etc.)
+
+#### Corpus
+- `doc_id` (string): Unique identifier for each document
+- `title` (string): Document title
+- `text` (string): Full document content
+- `url` (string): Source URL of the document
+
+#### Relevance Judgments (qrels)
+- `query_id` (string): Query identifier
+- `iteration` (int): Always 0 (standard TREC format)
+- `doc_id` (string): Document identifier  
+- `relevance` (int): Relevance score (0-3, where 3 is highly relevant)
+
+## 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?"
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("abdoelsayed/FutureQueryEval")
+
+# Access different splits
+queries = dataset["queries"]
+corpus = dataset["corpus"] 
+qrels = dataset["qrels"]
+
+# Example: Get first query
+print(f"Query: {queries[0]['query_text']}")
+print(f"Category: {queries[0]['category']}")
+```
+
+### Evaluation Example
+
+```python
+import pandas as pd
+
+# Load relevance judgments
+qrels_df = pd.read_csv("qrels.txt", sep=" ", 
+                      names=["query_id", "iteration", "doc_id", "relevance"])
+
+# Filter for a specific query
+query_rels = qrels_df[qrels_df["query_id"] == "FQ001"]
+print(f"Relevant documents for query FQ001: {len(query_rels)}")
+```
+
+## Methodology
+
+### Data Collection Process
+
+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
+
+### Annotation Guidelines
+
+- **Highly Relevant (3)**: Document directly answers the query
+- **Relevant (2)**: Document partially addresses the query  
+- **Marginally Relevant (1)**: Document mentions query topics but lacks detail
+- **Not Relevant (0)**: Document does not address the query
+
+## Research Applications
+
+This dataset is designed for:
+
+- **Reranker Evaluation**: Testing generalization to novel content
+- **Temporal IR Research**: Understanding time-sensitive retrieval challenges
+- **Domain Robustness**: Evaluating cross-domain performance
+- **Contamination Studies**: Clean evaluation on post-training data
+
+## Benchmark Results
+
+Top performing methods on FutureQueryEval:
+
+| Method | Type | NDCG@10 | Runtime (s) |
+|--------|------|---------|-------------|
+| Zephyr-7B | Listwise | **62.65** | 1,240 |
+| MonoT5-3B | Pointwise | **60.75** | 486 |
+| Flan-T5-XL | Setwise | **56.57** | 892 |
+
+## Dataset Updates
+
+FutureQueryEval will be updated every 6 months with new queries about recent events to maintain temporal novelty:
+
+- **Version 1.1** (December 2025): +100 queries from July-September 2025
+- **Version 1.2** (June 2026): +100 queries from October 2025-March 2026
+
+## Citation
+
+If you use FutureQueryEval in your research, please cite:
+
+```bibtex
+@misc{abdallah2025good,
+    title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models},
+    author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt},
+    year={2025},
+    eprint={2508.16757},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Contact
+
+- **Authors**: Abdelrahman Abdallah, Bhawna Piryani
+- **Institution**: University of Innsbruck
+- **Paper**: [arXiv:2508.16757](https://arxiv.org/abs/2508.16757)
+- **Code**: [GitHub Repository](https://github.com/DataScienceUIBK/llm-reranking-generalization-study)
+
+## License
+
+This dataset is released under the Apache-2.0 License.