DeepSeek's WEIRD Behavior: The cultural alignment of Large Language Models and the effects of prompt language and cultural prompting
James Luther, Donald Brown

TL;DR
This study examines how large language models align with US and Chinese cultures using Hofstede's survey data, revealing varying responsiveness to prompt language and cultural prompts across models.
Contribution
It introduces a methodology for assessing and influencing LLMs' cultural alignment through prompt engineering and cultural prompting strategies.
Findings
DeepSeek and GPT-5 align closely with US responses.
GPT-4's alignment shifts with prompt language and cultural prompts.
Low-cost models respond to language and cultural prompts to adjust alignment.
Abstract
Culture is a core component of human-to-human interaction and plays a vital role in how we perceive and interact with others. Advancements in the effectiveness of Large Language Models (LLMs) in generating human-sounding text have greatly increased the amount of human-to-computer interaction. As this field grows, the cultural alignment of these human-like agents becomes an important field of study. Our work uses Hofstede's VSM13 international surveys to understand the cultural alignment of the following models: DeepSeek-V3, V3.1, GPT-4, GPT-4.1, GPT-4o, and GPT-5. We use a combination of prompt language and cultural prompting, a strategy that uses a system prompt to shift a model's alignment to reflect a specific country, to align these LLMs with the United States and China. Our results show that DeepSeek-V3, V3.1, and OpenAI's GPT-5 exhibit a close alignment with the survey responses…
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Taxonomy
TopicsTopic Modeling · AI in Service Interactions · Artificial Intelligence in Healthcare and Education
