GPT-4o Mini extracts 6-41 times more usable Hausa and Fongbe text per API call than Gemini 2.5 Flash, with optimal elicitation strategies differing by language.
Available: https://arxiv.org/abs/2003.11529
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Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
A survey catalogs text and speech resources for Hausa and Fongbe, documenting sizes, domains, licensing, and gaps including limited Fongbe text diversity and missing Hausa speech corpora.
This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.
citing papers explorer
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Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe
GPT-4o Mini extracts 6-41 times more usable Hausa and Fongbe text per API call than Gemini 2.5 Flash, with optimal elicitation strategies differing by language.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
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A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
A survey catalogs text and speech resources for Hausa and Fongbe, documenting sizes, domains, licensing, and gaps including limited Fongbe text diversity and missing Hausa speech corpora.
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Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.