abdoelsayed/FutureQueryEval
FutureQueryEval Dataset (EMNLP 2025)π 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 referβ¦ See the full description on the dataset page: https://huggingface.co/datasets/abdoelsayed/FutureQueryEval.
| Author / organization | abdoelsayed |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 222 (as reported by Hugging Face) |
| Likes | 2 |
| Created | 22 August 2025 |
| Last updated at source | 28 August 2025 |
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- 16 Oct 2026 Description: FutureQueryEval Dataset (EMNLP 2025)π 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 pai β FutureQueryEval Dataset (EMNLP 2025)π 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 pai
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