Cultural Alignment in Large Language Models: An Explanatory Analysis
  Based on Hofstede's Cultural Dimensions
Reem I. Masoud Ziquan Liu Martin Ferianc Philip Treleaven Miguel Rodrigues
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.