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<p>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.</p>

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A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Ziyang Chen,
  • Erxue Min,
  • Xiang Zhao,
  • Yunxin Li,
  • Xin Jia,
  • Jinzhi Liao,
  • Jichao Li,
  • Shuaiqiang Wang,
  • Baotian Hu,
  • Dawei Yin

摘要

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.