Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages
Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages, yet their performance on low-resource African languages remains poorly understood and largely unevaluated. This paper presents Nsanku, a systematic benchmark that evaluates the zero-shot machine translation performance of 19 open-weight and proprietary LLMs across 43 Ghanaian languages paired with English. Evaluation sentences were sourced from the YouVersion Bible platform, providing 300 sentence pairs per language. Two complementary automatic metrics are employed: Bilingual Evaluation Understudy (BLEU) and Character n-gram F-Score (chrF), alongside an average accuracy score ...
| Authors | Stephen E. Moore, Mich-Seth Owusu, Akwasi Asare et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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