Large Language Models (LLMs), such as ChatGPT, are widely used to generate content for various purposes and audiences. However, these models may not reflect the cultural and emotional diversity of their users, especially for low-resource languages. In this paper, we investigate how ChatGPT represents Hausa's culture and emotions. We compare responses generated by ChatGPT with those provided by native Hausa speakers on 37 culturally relevant questions. We conducted experiments using emotion analysis and applied two similarity metrics to measure the alignment between human and ChatGPT responses. We also collected human participants ratings and feedback on ChatGPT responses. Our results show that ChatGPT has some level of similarity to human responses, but also exhibits some gaps and biases in its knowledge and awareness of the Hausa culture and emotions. We discuss the implications and limitations of our methodology and analysis and suggest ways to improve the performance and evaluation of LLMs for low-resource languages.

我们研究了ChatGPT对豪萨文化和情感的表达方式，通过比较生成回答和豪萨本地人在37个与文化相关的问题上的回答，以及运用情感分析和相似性度量等方法进行实验和评估，结果显示ChatGPT在某种程度上和人类回答相似，但同时也存在知识和文化认知方面的不足和偏见。我们讨论了我们方法和分析的意义和局限，并提出了改进低资源语言下大语言模型性能和评估的方法。

生成语言模型能否多元文化化？基于ChatGPT对豪萨文化和情感的研究