In this paper, we identify 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 that consists of both 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 for critical examination
of cultural dominance and ethical consideration in their development and
deployment. 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.

我们发现大型语言模型（LLMs）中存在文化主导问题，由于在模型训练中主要使用英文数据（例如 ChatGPT），当用户使用非英语语言进行提问时，LLMs 常常会提供与预期文化不相关的不适当的英语文化相关答案。为了系统评估文化主导问题，我们构建了一个基准，其中包括具体的（例如节日和歌曲）和抽象的（例如价值观和意见）文化对象。实证结果表明，代表性的 GPT 模型存在文化主导问题，其中 GPT-4 受到的影响最大，而 text-davinci-003 受到的影响最小。我们的研究强调了对文化主导问题进行批判性审视和在开发和部署中考虑道德问题的必要性。我们展示了在 LLMs 中如何通过两种直接的方法（即使用更多样化的数据进行预训练和文化感知的提示）来显著减轻文化主导问题。