ACL 2025long0 citations

Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models

Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo

Abstract

New LLM benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of LLMs to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, first, we focus on the Chinese language over 6 major topics with 99 diverse subtopics. Second, we conduct a comprehensive quality control process to achieve high-quality questions and answers, where the reference answers are static and cannot be changed over time. Third, following SimpleQA, the questions and answers are very short, and the grading process is easy-to-evaluate. Based on Chinese SimpleQA, we perform a comprehensive evaluation of the factuality abilities of existing LLMs. Finally, we hope that Chinese SimpleQA could guide the developers to better understand the Chinese factuality abilities of their models and facilitate the growth of LLMs.

BibTeX
@inproceedings{he-etal-2025-chinese,
    title = "{C}hinese {S}imple{QA}: A {C}hinese Factuality Evaluation for Large Language Models",
    author = "He, Yancheng  and
      Li, Shilong  and
      Liu, Jiaheng  and
      Tan, Yingshui  and
      Wang, Weixun  and
      Huang, Hui  and
      Bu, Xingyuan  and
      Guo, Hangyu  and
      Hu, Chengwei  and
      Zheng, Boren  and
      Lin, Zhuoran  and
      Sun, Dekai  and
      Zheng, Zhicheng  and
      Su, Wenbo  and
      Zheng, Bo",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.941/",
    doi = "10.18653/v1/2025.acl-long.941",
    pages = "19182--19208",
    ISBN = "979-8-89176-251-0"
}
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models · ACL 2025