一个问题回答数据集的时间敏感的检索-增长的世代
Ziyang Chen1, Erxue Min2, Xiang Zhao3
1National Key Laboratory of Big Data and Decision, National University of Defense Technology, Changsha, China.
Scientific data
|November 21, 2025
概括
ChronoQA 是一种新的中文问答数据集,用于评估检索增强生成 (RAG) 系统中的时间推理. 它使用2019-2024年的新闻文章来测试RAG在各种基于时间的问题上的表现.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 评估时间推理对于先进的问答系统至关重要.
- 现有的基准可能无法充分解决动态知识领域的时间理解的复杂性.
- 检索增强生成 (RAG) 系统需要强大的时间推理能力.
研究的目的:
- 介绍ChronoQA,这是一个针对中文问答的新型基准数据集.
- 具体评估检索增强生成 (RAG) 系统的时间推理能力.
- 为在不断变化的信息环境中对RAG系统进行基准测试提供资源.
主要方法:
- 从超过30万篇中国新闻文章 (2019-2024) 构建了ChronoQA.
- 开发了5176个问题,包括绝对,总和相对时间类型.
- 包括含有明确和隐含时间表达式的问题.
- 设计用于单个和多个文档问答场景.
主要成果:
- ChronoQA 提供了一套全面的时间推理挑战.
- 该数据集支持评估RAG的时间对齐和逻辑一致性.
- 它为临时QA研究提供了一个可扩展和可靠的资源.
结论:
- 在中国RAG系统中,ChronoQA为评估时间推理提供了一个重要的基准.
- 数据集促进了人工智能更复杂的时间理解的发展.
- ChronoQA 解决了在动态,时间敏感的信息环境中评估人工智能系统的需求.
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