  This paper identifies a cultural dominance issue within large language models
(LLMs) due to the predominant use of English data in model training (e.g.,
ChatGPT). LLMs often provide inappropriate English-culture-related answers that
are not relevant to the expected culture when users ask in non-English
languages. To systematically evaluate the cultural dominance issue, we build a
benchmark of concrete (e.g., holidays and songs) and abstract (e.g., values and
opinions) cultural objects. Empirical results show that the representative GPT
models suffer from the culture dominance problem, where GPT-4 is the most
affected while text-davinci-003 suffers the least from this problem. Our study
emphasizes the need to critically examine cultural dominance and ethical
consideration in their development and deployment. We show that 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.
