Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He
Abstract
With the rapid advancement of Large Language Models (LLMs), significant safety concerns have emerged. Fundamentally, the safety of large language models is closely linked to the accuracy, comprehensiveness, and clarity of their understanding of safety knowledge, particularly in domains such as law, policy and ethics. This factuality ability is crucial in determining whether these models can be deployed and applied safely and compliantly within specific regions. To address these challenges and better evaluate the factuality ability of LLMs to answer short question, we introduce the Chinese SafetyQA benchmark. Chinese SafetyQA has several properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate, safety-related, harmless). Based on Chinese SafetyQA, we perform a comprehensive evaluation on the factuality abilities of existing LLMs and analyze how these capabilities relate to LLM abilities, e.g., RAG ability and robustness against attacks.
BibTeX
@inproceedings{tan-etal-2025-chinese,
title = "{C}hinese {S}afety{QA}: A Safety Short-form Factuality Benchmark for Large Language Models",
author = "Tan, Yingshui and
Zheng, Boren and
Zheng, Baihui and
Cao, Kerui and
Jing, Huiyun and
Wei, Jincheng and
Liu, Jiaheng and
He, Yancheng and
Su, Wenbo and
Zhu, Xiaoyong and
Zheng, Bo and
Zhang, Kaifu",
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.732/",
doi = "10.18653/v1/2025.acl-long.732",
pages = "15053--15076",
ISBN = "979-8-89176-251-0"
}