A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
We introduce ChronoQA, a benchmark dataset for Chinese question answering focused on evaluating temporal reasoning in Retrieval-Augmented Generation (RAG) systems. Built from over 300,000 news articles published between 2019 and 2024, ChronoQA contains 5,176 questions covering absolute, aggregate, and relative temporal types, with both explicit and implicit time expressions. The dataset features both single- and multi-document scenarios, reflecting real-world requirements for temporal alignment and logical consistency. By providing structured evaluation across a wide range of temporal tasks, ChronoQA offers a dynamic, reliable, and scalable resource for benchmarking RAG systems in evolving knowledge environments.
Authors
- Erxue Min (ORCID: https://orcid.org/0000-0002-1972-6608)
- Xiang Zhao (ORCID: https://orcid.org/0000-0001-6339-0219)
- Yunxin Li (ORCID: https://orcid.org/0009-0006-4833-4247)
- Shuaiqiang Wang (ORCID: https://orcid.org/0000-0002-9212-1947)
- Jinzhi Liao (ORCID: https://orcid.org/0000-0002-2898-6559)
- Dawei Yin (ORCID: https://orcid.org/0000-0002-8846-2001)
- Baotian Hu (ORCID: https://orcid.org/0000-0001-7490-684X)
- Ziyang Chen (ORCID: https://orcid.org/0000-0002-1714-0304)
- Jichao Li (ORCID: https://orcid.org/0000-0002-1781-3732)
- Xin Jia (ORCID: https://orcid.org/0009-0000-1736-4036)
Institutions
- National University of Defense Technology (CN)
- Harbin Institute of Technology (CN)
- Baidu (China) (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1038/s41597-025-06098-y
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China