Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT
This work addresses cultural and emotional diversity issues in LLMs for low-resource languages like Hausa, but it is incremental as it focuses on evaluation rather than new methods.
The study investigated ChatGPT's representation of Hausa culture and emotions by comparing its responses to those of native speakers on 37 questions, finding some similarity but also gaps and biases.
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.