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Found 4 papers, 2 papers with code
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?
Our results suggest that all LLMs produce below-par performance on African languages, and there is a large gap in performance compared to high-resource languages like English most tasks.
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).
