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In this paper, we identify a cultural dominance issue within large languagemodels (LLMs) due to the predominant use of English data in model training(e.g. ChatGPT). LLMs often provide inappropriate English-culture-relatedanswers that are not relevant to the expected culture when users ask innon-English languages. To systematically evaluate the cultural dominance issue,we build a benchmark that consists of both concrete (e.g. holidays and songs)and abstract (e.g. values and opinions) cultural objects. Empirical resultsshow that the representative GPT models suffer from the culture dominanceproblem, where GPT-4 is the most affected while text-davinci-003 suffers theleast from this problem. Our study emphasizes the need for critical examinationof cultural dominance and ethical consideration in their development anddeployment. We show two straightforward methods in model development (i.e.pretraining on more diverse data) and deployment (e.g. culture-aware prompting)can significantly mitigate the cultural dominance issue in LLMs.

Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models