Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models

• URL: http://arxiv.org/abs/2412.15265v2
• Date: Mon, 23 Dec 2024 11:06:56 GMT
• Title: Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
• Authors: Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiangyong Zhu, Bo Zheng, Kaifu Zhang,
• Abstract summary: The safety of large language models is closely linked to their accuracy, comprehensiveness, and clarity of their understanding of safety knowledge. 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 questions, we introduce the Chinese SafetyQA benchmark.
• Score: 18.056739637824954
• License:
• 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 questions, 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.

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