The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals and societies with diverse cultural backgrounds. While the discourse has focused mainly on political and social biases, our research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify 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 quantitatively evaluate LLMs, namely Llama 2, GPT-3.5, and GPT-4, against the cultural dimensions of regions like the United States, China, and Arab countries, using different prompting styles and exploring the effects of language-specific fine-tuning on the models' behavioural tendencies and cultural values. Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions. Our study demonstrates that while all LLMs struggle to grasp cultural values, GPT-4 shows a unique capability to adapt to cultural nuances, particularly in Chinese settings. However, it faces challenges with American and Arab cultures. The research also highlights that fine-tuning LLama 2 models with different languages changes their responses to cultural questions, emphasizing the need for culturally diverse development in AI for worldwide acceptance and ethical use. For more details or to contribute to this research, visit our GitHub page https://github.com/reemim/Hofstedes_CAT/

翻译：大型语言模型（LLMs）的部署引发了对其文化错位及可能对具有不同文化背景的个人与社会造成影响的担忧。虽然现有讨论主要聚焦于政治和社会偏见，但本研究提出了一种文化对齐测试（霍夫斯泰德CAT），通过霍夫斯泰德文化维度框架量化文化对齐，该框架利用潜变量分析提供了跨文化比较的解释性视角。我们采用该方法定量评估了Llama 2、GPT-3.5和GPT-4等大型语言模型，针对美国、中国和阿拉伯国家等地区的文化维度，使用了不同的提示风格，并探讨了特定语言微调对模型行为倾向和文化价值观的影响。研究结果量化了大型语言模型的文化对齐程度，并揭示了不同模型在解释性文化维度上的差异。研究表明，尽管所有模型在理解文化价值观方面均存在困难，但GPT-4展现出独特的适应文化细微差别的能力，尤其是在中文场景中。然而，它在应对美国和阿拉伯文化时面临挑战。该研究还强调，使用不同语言对Llama 2模型进行微调会改变其对文化问题的回答，凸显了在人工智能开发中融入文化多样性以实现全球接受和伦理使用的必要性。如需了解更多详情或为本研究做出贡献，请访问我们的GitHub页面：https://github.com/reemim/Hofstedes_CAT/