@inproceedings{ahmad-etal-2024-generative,
title = "Are Generative Language Models Multicultural? A Study on {H}ausa Culture and Emotions using {C}hat{GPT}",
author = "Ahmad, Ibrahim and
Dudy, Shiran and
Ramachandranpillai, Resmi and
Church, Kenneth",
editor = "Prabhakaran, Vinodkumar and
Dev, Sunipa and
Benotti, Luciana and
Hershcovich, Daniel and
Cabello, Laura and
Cao, Yong and
Adebara, Ife and
Zhou, Li",
booktitle = "Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.c3nlp-1.8",
pages = "98--106",
abstract = "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. We also used two similarity metrics to measure the alignment between human and ChatGPT responses. We also collect 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 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.",
}
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<abstract>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. We also used two similarity metrics to measure the alignment between human and ChatGPT responses. We also collect 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 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.</abstract>
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%0 Conference Proceedings
%T Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT
%A Ahmad, Ibrahim
%A Dudy, Shiran
%A Ramachandranpillai, Resmi
%A Church, Kenneth
%Y Prabhakaran, Vinodkumar
%Y Dev, Sunipa
%Y Benotti, Luciana
%Y Hershcovich, Daniel
%Y Cabello, Laura
%Y Cao, Yong
%Y Adebara, Ife
%Y Zhou, Li
%S Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ahmad-etal-2024-generative
%X 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. We also used two similarity metrics to measure the alignment between human and ChatGPT responses. We also collect 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 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.
%U https://aclanthology.org/2024.c3nlp-1.8
%P 98-106
Markdown (Informal)
[Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT](https://aclanthology.org/2024.c3nlp-1.8) (Ahmad et al., C3NLP-WS 2024)
ACL