Linguistic Challenges in Generative Artificial Intelligence: Implications for Low-Resource Languages in the Developing World

Nir Kshetri

Journal of Global Information Technology Management, 2024, vol. 27, issue 2, 95-99

Abstract: Proficiency in English is pivotal for leveraging information and communication technologies, but it holds even greater significance in the realm of generative artificial intelligence (GAI), which is poised as the next digital frontier. However, the dominance of English in benchmarks and training data for large language models (LLMs) exacerbates challenges for individuals and organizations in the developing world, predominantly non-English speakers. Despite the commendable performance of GAI in select developed languages like French, Spanish, and Japanese, it struggles to deliver comparable results in low-resource languages (LRLs) such as Bengali, Hindi, and Swahili. These languages, deprived of adequate online content, face obstacles in training specialized models due to script complexities and limited lexical resources. While countries like Japan and Iceland offer promising models for addressing linguistic challenges, the road ahead necessitates collaborative efforts to develop LLMs tailored for LRLs and rectify linguistic inaccuracies, ensuring inclusive and equitable AI development.

Date: 2024
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DOI: 10.1080/1097198X.2024.2341496

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