Dekai Sun


2025

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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 | Chengwei Hu | Boren Zheng | Zhuoran Lin | Dekai Sun | Zhicheng Zheng | Wenbo Su | Bo Zheng
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.