SC-Safety: A Multi-round Open-ended Question Adversarial Safety
  Benchmark for Large Language Models in Chinese
Liang Xu Kangkang Zhao Lei Zhu Hang Xue
Large language models (LLMs), like ChatGPT and GPT-4, have demonstrated
remarkable abilities in natural language understanding and generation. However,
alongside their positive impact on our daily tasks, they can also produce
harmful content that negatively affects societal perceptions. To systematically
assess the safety of Chinese LLMs, we introduce SuperCLUE-Safety (SC-Safety) -
a multi-round adversarial benchmark with 4912 open-ended questions covering
more than 20 safety sub-dimensions. Adversarial human-model interactions and
conversations significantly increase the challenges compared to existing
methods. Experiments on 13 major LLMs supporting Chinese yield the following
insights: 1) Closed-source models outperform open-sourced ones in terms of
safety; 2) Models released from China demonstrate comparable safety levels to
LLMs like GPT-3.5-turbo; 3) Some smaller models with 6B-13B parameters can
compete effectively in terms of safety. By introducing SC-Safety, we aim to
promote collaborative efforts to create safer and more trustworthy LLMs. The
benchmark and findings provide guidance on model selection. Our benchmark can
be found at https://www.CLUEbenchmarks.com