The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals from various cultural norms. Existing work investigated political and social biases and public opinions rather than their cultural values. To address this limitation, the proposed Cultural Alignment Test (CAT) quantifies cultural alignment using Hofstede's cultural dimension framework, which offers an explanatory cross-cultural comparison through the latent variable analysis. We apply our approach to assess the cultural values embedded in state-of-the-art LLMs, such as: ChatGPT and Bard, across diverse cultures of countries: United States (US), Saudi Arabia, China, and Slovakia, using different prompting styles and hyperparameter settings. Our results not only quantify cultural alignment of LLMs with certain countries, but also reveal the difference between LLMs in explanatory cultural dimensions. While all LLMs did not provide satisfactory results in understanding cultural values, GPT-4 exhibited the highest CAT score for the cultural values of the US.

使用Hofstede的文化维度框架，通过潜变量分析提供解释性跨文化比较，文中提出了Cultural Alignment Test（CAT）以解决大型语言模型（LLMs）的文化不匹配问题，并使用不同的提示样式和超参数设置评估了ChatGPT和Bard等LLMs嵌入在美国、沙特阿拉伯、中国和斯洛伐克等不同文化之中的文化价值。结果不仅量化了LLMs与特定国家的文化一致性，还揭示了LLMs在解释性文化维度方面的差异。其中GPT-4在理解美国文化价值方面得分最高。

大型语言模型中的文化整合：基于霍夫斯泰德文化维度的分析