@inproceedings{oppong-etal-2025-examining,
    title = "Examining the Cultural Encoding of Gender Bias in {LLM}s for Low-Resourced {A}frican Languages",
    author = "Oppong, Abigail  and
      Nigatu, Hellina Hailu  and
      Okolo, Chinasa T.",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.gebnlp-1.31/",
    doi = "10.18653/v1/2025.gebnlp-1.31",
    pages = "358--378",
    ISBN = "979-8-89176-277-0",
    abstract = "Large Language Models (LLMs) are deployed in several aspects of everyday life. While the technology could have several benefits, like many socio-technical systems, it also encodes several biases. Trained on large, crawled datasets from the web, these models perpetuate stereotypes and regurgitate representational bias that is rampant in their training data. Languages encode gender in varying ways; some languages are grammatically gendered, while others do not. Bias in the languages themselves may also vary based on cultural, social, and religious contexts. In this paper, we investigate gender bias in LLMs by selecting two languages, Twi and Amharic. Twi is a non-gendered African language spoken in Ghana, while Amharic is a gendered language spoken in Ethiopia. Using these two languages on the two ends of the continent and their opposing grammatical gender system, we evaluate LLMs in three tasks: Machine Translation, Image Generation, and Sentence Completion. Our results give insights into the gender bias encoded in LLMs using two low-resourced languages and broaden the conversation on how culture and social structures play a role in disparate system performances."
}