  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/
