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.

翻译：随着大型语言模型的快速发展，人们日益担忧其可能带来的风险或负面社会影响。因此，对人类价值观对齐的评估变得愈发重要。以往工作主要关注大型语言模型在特定知识与推理能力上的表现，而忽略了对人类价值观的对齐，尤其是在中文语境中。本文提出了CValues——首个中文人类价值观评估基准，用于从安全性与责任性两个维度衡量大型语言模型的对齐能力。为此，我们通过专业专家在10个场景中人工收集了对抗性安全提示，并从8个领域诱导出责任性提示。为全面评估中文大型语言模型的价值观，我们不仅通过人工评估实现可靠对比，还构建了用于自动评估的多选题提示。研究发现：多数中文大型语言模型在安全性方面表现良好，但在责任性方面仍有较大提升空间。此外，自动评估与人工评估在评估不同层面的人类价值观对齐中均具有重要价值。该基准及代码已在ModelScope与Github平台开源。