Datasets:

abdoelsayed
/

FutureQueryEval

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README.md CHANGED Viewed

@@ -1,204 +1,204 @@
----
-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
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path:
-    - queries.tsv
----
-# 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.

+---
+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
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path:
+    - queries.tsv
+---
+# 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.