Mich-Seth Owusu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28173 · open-access PDF
Model Bahasa Besar (LLM) pada dasarnya bergantung pada keragaman, kedalaman, dan kekakuan fakta dari corpora pra-latihan mereka. Namun, dataset pra-latihan dasar kontemporer (seperti The Pile, S2ORC, dan Dolma) menunjukkan kelalaian struktural yang parah: beasiswa ilmiah, medis, hukum, dan pertanian Afrika membentuk kurang dari 0,5% dari token pra-latihan global. Oleh karena itu, model state-of-the-art menderita mode kegagalan sistematis ketika berpendapat tentang epidemiologi tropis, agro-ekologi petani kecil lokal, pemerintahan ekonomi regional, dan struktur linguistik asli
Machine translation (Meta M2M100); the English original follows.
Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary foundational pre-training datasets (such as The Pile, S2ORC, and Dolma) exhibit a severe structural omission: African scientific, medical, legal, and agricultural scholarship constitutes less than 0.5% of global pre-training tokens. Consequently, state-of-the-art models suffer from systematic failure modes when reasoning about tropical epidemiology, localized smallholder agro-ecologies, regional economic governance, and indigenous linguistic structur
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