Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language
  Models
Authors: name: Yancheng He, name: Shilong Li, name: Jiaheng Liu, name: Yingshui Tan, name: Hui Huang, name: Weixun Wang, name: Xingyuan Bu, name: Hangyu Guo, name: Chengwei Hu, name: Boren Zheng, name: Xuepeng Liu, name: Dekai Sun, name: Wenbo Su, name: Bo Zheng

New LLM evaluation 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 language models 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 OpenAI API. Based on Chinese SimpleQA, we
perform a comprehensive evaluation on 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 foundation models.