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 the key data resource for improving LLMs' Chinese
question-answering ability in the future.

为了提高中文大型语言模型（LLMs）的问答能力，本文引入了 CDQA，这是一个包含与中国互联网最新新闻相关的问答对的中文动态问答基准，通过人工和模型相结合的流程获得高质量的数据，根据答案变化的频率仔细分类样本以便更精细地观察 LLMs 的能力，我们还评估和分析了 CDQA 上的主流和先进的中文 LLMs，广泛的实验和有价值的见解表明我们提出的 CDQA 是具有挑战性和值得进一步研究的，我们相信我们提供的基准将成为未来改善 LLMs 中文问答能力的关键数据资源。