# Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions

**Authors:** Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip Treleaven, Miguel Rodrigues  
**arXiv:** [2309.12342](https://arxiv.org/abs/2309.12342)  
**Date:** 2023-08-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.12342v1/

## Key points

- Authors Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip Treleaven, and Miguel Rodrigues focus on cultural misalignment in large language models (LLMs) and its impact on diverse cultural backgrounds.
- Introduces the Cultural Alignment Test (CAT) as a novel method to quantify cultural alignment using Hofstede's cultural dimension framework.
- Applies the CAT to evaluate cultural values in cutting-edge LLMs like ChatGPT and Bard across four distinct cultures: US, Saudi Arabia, China, and Slovakia.
- GPT-4 emerges as the top performer with the highest CAT score for capturing US cultural values.
- Highlights the importance of considering cultural alignment when deploying LLMs globally and emphasizes the need for further research to enhance cross-cultural understanding within AI technologies.

## Summary

In their paper titled "Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions," authors Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip Treleaven, and Miguel Rodrigues delve into the pressing issue of cultural misalignment in large language models (LLMs) and its potential impact on individuals from diverse cultural backgrounds. Previous studies have focused on political biases and social opinions within LLMs; however, this research introduces the Cultural Alignment Test (CAT) as a novel method to quantify cultural alignment using Hofstede's cultural dimension framework. The study applies the CAT to evaluate the cultural values embedded in cutting-edge LLMs like ChatGPT and Bard across four distinct cultures: the United States (US), Saudi Arabia, China, and Slovakia. By employing various prompting styles and hyperparameter settings, the researchers aim to provide a comprehensive analysis of how well these LLMs align with different cultural norms. Through their analysis, the authors not only quantify the level of cultural alignment between LLMs and specific countries but also uncover significant differences in explanatory cultural dimensions among these models. Despite some limitations in fully grasping cultural values, GPT-4 emerges as the top performer with the highest CAT score for capturing US cultural values. This study sheds light on the importance of considering cultural alignment when deploying LLMs globally and underscores the need for further research to enhance cross-cultural understanding within artificial intelligence technologies. The findings offer valuable insights for developers and policymakers striving to create more culturally sensitive AI systems in an increasingly interconnected world.