We introduce ChronoQA, a large-scale benchmark dataset for Chinese question answering, specifically designed to evaluate temporal reasoning in Retrieval-Augmented Generation (RAG) systems. ChronoQA is constructed from over 300,000 news articles published between 2019 and 2024, and contains 5,176 high-quality questions covering absolute, aggregate, and relative temporal types with both explicit and implicit time expressions. The dataset supports both single- and multi-document scenarios, reflecting the real-world requirements for temporal alignment and logical consistency. ChronoQA features comprehensive structural annotations and has undergone multi-stage validation, including rule-based, LLM-based, and human evaluation, to ensure data quality. By providing a dynamic, reliable, and scalable resource, ChronoQA enables structured evaluation across a wide range of temporal tasks, and serves as a robust benchmark for advancing time-sensitive retrieval-augmented question answering systems.
@article{arxiv.2508.12282,
title = {A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation},
author = {Ziyang Chen and Erxue Min and Xiang Zhao and Yunxin Li and Xin Jia and Jinzhi Liao and Jichao Li and Shuaiqiang Wang and Baotian Hu and Dawei Yin},
journal= {arXiv preprint arXiv:2508.12282},
year = {2025}
}