并非所有国家都庆祝感恩节：论大型语言模型中的文化主导现象
Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models
AI总结：
本文揭示了大型语言模型因训练数据以英语为主而产生的文化主导问题，通过构建包含具体与抽象文化对象的基准进行评估，并提出数据多样化预训练和文化感知提示两种方法显著缓解了该问题。
摘要：
本文指出了大型语言模型（LLMs）中由于在模型训练（如ChatGPT）中主要使用英语数据而产生的文化主导问题。当用户使用非英语语言提问时，LLMs常常提供与预期文化无关的、不恰当的英语文化相关答案。为了系统地评估文化主导问题，我们构建了一个包含具体（如节日和歌曲）及抽象（如价值观和观点）文化对象的基准。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受影响最大，而text-davinci-003受此问题影响最小。我们的研究强调，在其开发和部署过程中，亟需批判性地审视文化主导及伦理考量。我们展示了模型开发（即在更多样化数据上预训练）和部署（如文化感知提示）中的两种直接方法，能显著缓解LLMs中的文化主导问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
