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Found 6 papers, 1 papers with code
InkubaLM: A small language model for low-resource African languages
High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints.
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages.
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments.
How good are Large Language Models on African Languages?
Similarly, we find the recent Aya model to have comparable result to mT0 in almost all tasks except for topic classification where it outperform mT0.
How Good are Commercial Large Language Models on African Languages?
We present a preliminary analysis of commercial large language models on two tasks (machine translation and text classification) across eight African languages, spanning different language families and geographical areas.
MasakhaNEWS: News Topic Classification for African languages
Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API).
