| --- |
| license: apache-2.0 |
| task_categories: |
| - text-retrieval |
| language: |
| - en |
| tags: |
| - information-retrieval |
| - reranking |
| - temporal-evaluation |
| - benchmark |
| size_categories: |
| - 1K<n<10K |
| pretty_name: Reranking, Retreiver |
| --- |
| |
| # FutureQueryEval Dataset (EMNLP 2025)🔍 |
|
|
| ## 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. |
