CValues: Measuring the Values of Chinese Large Language Models from
  Safety to Responsibility
Guohai Xu Jiayi Liu Ming Yan Haotian Xu Jinghui Si Zhuoran Zhou Peng Yi Xing Gao Jitao Sang Rong Zhang Ji Zhang Chao Peng Fei Huang Jingren Zhou
With the rapid evolution of large language models (LLMs), there is a growing
concern that they may pose risks or have negative social impacts. Therefore,
evaluation of human values alignment is becoming increasingly important.
Previous work mainly focuses on assessing the performance of LLMs on certain
knowledge and reasoning abilities, while neglecting the alignment to human
values, especially in a Chinese context. In this paper, we present CValues, the
first Chinese human values evaluation benchmark to measure the alignment
ability of LLMs in terms of both safety and responsibility criteria. As a
result, we have manually collected adversarial safety prompts across 10
scenarios and induced responsibility prompts from 8 domains by professional
experts. To provide a comprehensive values evaluation of Chinese LLMs, we not
only conduct human evaluation for reliable comparison, but also construct
multi-choice prompts for automatic evaluation. Our findings suggest that while
most Chinese LLMs perform well in terms of safety, there is considerable room
for improvement in terms of responsibility. Moreover, both the automatic and
human evaluation are important for assessing the human values alignment in
different aspects. The benchmark and code is available on ModelScope and
Github.