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

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

Summary

This summary is machine-generated.

ChronoQA is a new Chinese question-answering dataset for evaluating temporal reasoning in Retrieval-Augmented Generation (RAG) systems. It uses news articles from 2019-2024 to test RAG performance on diverse time-based questions.

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Published on: January 23, 2017

Area of Science:

Natural Language Processing
Artificial Intelligence
Information Retrieval

Background:

Evaluating temporal reasoning is crucial for advanced question-answering systems.
Existing benchmarks may not adequately address the complexities of temporal understanding in dynamic knowledge domains.
Retrieval-Augmented Generation (RAG) systems require robust temporal reasoning capabilities.

Purpose of the Study:

Introduce ChronoQA, a novel benchmark dataset for Chinese question answering.
Specifically evaluate the temporal reasoning abilities of Retrieval-Augmented Generation (RAG) systems.
Provide a resource for benchmarking RAG systems in evolving information environments.

Main Methods:

Constructed ChronoQA from over 300,000 Chinese news articles (2019-2024).
Developed 5,176 questions encompassing absolute, aggregate, and relative temporal types.
Included questions with both explicit and implicit time expressions.
Designed for both single- and multi-document question-answering scenarios.

Main Results:

ChronoQA features a comprehensive set of temporal reasoning challenges.
The dataset supports evaluation of temporal alignment and logical consistency in RAG.
It offers a scalable and reliable resource for temporal QA research.

Conclusions:

ChronoQA provides a vital benchmark for assessing temporal reasoning in Chinese RAG systems.
The dataset facilitates the development of more sophisticated temporal understanding in AI.
ChronoQA addresses the need for evaluating AI systems in dynamic, time-sensitive information landscapes.