构建中文时间敏感问答数据集，评估RAG系统时序推理能力
A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
- 从2019-2024年超30万篇新闻构建，覆盖绝对、相对、聚合三类时间问题
- 含5176个高质量问题，支持单/多文档场景，验证时序对齐与逻辑一致性
- 经规则、大模型和人工三重验证，适合作为时间敏感RAG系统的基准
我们提出ChronoQA，一个大规模中文问答数据集，专门用于评估检索增强生成（RAG）系统中的时序推理能力。该数据集基于2019至2024年间超过30万篇新闻文章构建，包含5,176个高质量问题，涵盖绝对、聚合和相对时间类型，且包含显式与隐式时间表达。数据集支持单文档与多文档场景，反映真实世界中对时序对齐与逻辑一致性的需求。ChronoQA具备全面的结构化标注，并经过规则、大语言模型及人工三阶段验证，确保数据质量。该数据集提供动态、可靠且可扩展的评估资源，支持广泛的时间任务评测，是推进时间敏感检索增强问答系统的重要基准。
原文摘要 · Abstract (English)
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.
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