The American Ghost in the Machine: How language models align culturally and the effects of cultural prompting

TL;DR

This paper investigates the cultural alignment of popular large language models using Hofstede's dimensions and demonstrates that cultural prompts can effectively shift models' cultural biases, though some cultures remain challenging.

Contribution

It introduces a method to assess and modify the cultural alignment of LLMs using cultural prompting based on Hofstede's dimensions.

Findings

Most models favor the US culture without prompts.

Cultural prompting can shift models closer to target cultures.

Models struggle to align with Japanese and Chinese cultures.

Abstract

Culture is the bedrock of human interaction; it dictates how we perceive and respond to everyday interactions. As the field of human-computer interaction grows via the rise of generative Large Language Models (LLMs), the cultural alignment of these models become an important field of study. This work, using the VSM13 International Survey and Hofstede's cultural dimensions, identifies the cultural alignment of popular LLMs (DeepSeek-V3, V3.1, GPT-5, GPT-4.1, GPT-4, Claude Opus 4, Llama 3.1, and Mistral Large). We then use cultural prompting, or using system prompts to shift the cultural alignment of a model to a desired country, to test the adaptability of these models to other cultures, namely China, France, India, Iran, Japan, and the United States. We find that the majority of the eight LLMs tested favor the United States when the culture is not specified, with varying results when prompted for other cultures. When using cultural prompting, seven of the eight models shifted closer to the expected culture. We find that models had trouble aligning with Japan and China, despite two of the models tested originating with the Chinese company DeepSeek.