How good are Large Language Models on African Languages?
Jessica Ojo Kelechi Ogueji Pontus Stenetorp David I. Adelani
Recent advancements in natural language processing have led to the
proliferation of large language models (LLMs). These models have been shown to
yield good performance, using in-context learning, even on unseen tasks and
languages. Additionally, they have been widely adopted as
language-model-as-a-service commercial APIs like GPT-4 API. However, their
performance on African languages is largely unknown. We present an analysis of
three popular large language models (mT0, LLaMa 2, and GPT-4) on five tasks
(news topic classification, sentiment classification, machine translation,
question answering, and named entity recognition) across 30 African languages,
spanning different language families and geographical regions. 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. We find that GPT-4 has an average or impressive performance
on classification tasks but very poor results on generative tasks like machine
translation. Surprisingly, we find that mT0 had the best overall on
cross-lingual QA, better than the state-of-the-art supervised model (i.e.
fine-tuned mT5) and GPT-4 on African languages. Overall, LLaMa 2 records the
worst performance due to its limited multilingual capabilities and
English-centric pre-training corpus. In general, our findings present a
call-to-action to ensure African languages are well represented in large
language models, given their growing popularity.