Employing large language models in Swahili, a low-resource language

The article is potentially destined to examine the efficacy of Large Language Models (LLMs) in Swahili (standard dialect spoken in Tanzania), a relatively less privileged and Low-resource Language (LRL) that, to some extent, remains underrepresented in AI communication technologies. Despite the rapid growth in LLM use, Swahili users in Tanzania often encounter inaccuracies and unclear outputs, highlighting persistent challenges in model performance. The inaccuracies of LLMs in Swahili undoubtedly demonstrate the challenges associated with their use and effectiveness in such a language. In this study, participants from Tanzanian Swahili-speaking communities evaluated the models' outputs through usability tests. Findings reveal that apart from architectural limitations, data scarcity drives the ineffectiveness of the models. Frequent distortions, mostly in Swahili than in French confirms the need for broader multilingual inclusion in LLM training. The study highlights the imperative for inclusive AI development that empowers low-resource languages.