Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu
Abstract
How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer the latest dynamic questions well. To promote the improvement of Chinese LLMs’ ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet. We obtain high-quality data through a pipeline that combines humans and models, and carefully classify the samples according to the frequency of answer changes to facilitate a more fine-grained observation of LLMs’ capabilities. We have also evaluated and analyzed mainstream and advanced Chinese LLMs on CDQA. Extensive experiments and valuable insights suggest that our proposed CDQA is challenging and worthy of more further study. We believe that the benchmark we provide will become one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.
BibTeX
@inproceedings{xu-etal-2025-llms,
title = "Let {LLM}s Take on the Latest Challenges! A {C}hinese Dynamic Question Answering Benchmark",
author = "Xu, Zhikun and
Li, Yinghui and
Ding, Ruixue and
Wang, Xinyu and
Chen, Boli and
Jiang, Yong and
Zheng, Haitao and
Lu, Wenlian and
Xie, Pengjun and
Huang, Fei",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.695/",
pages = "10435--10448"
}