The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali

However, most of the world's languages, often referred to as "low-resource languages", still remain either not supported or insufficiently supported due to the limited availability of data and language resources, and market, economic, and global inequality factors. Mali, a multilingual country with 13 official languages, including Bamanankan (Bambara), Bomu, Bozo, Dɔgɔsɔ (Dogon), Fulfulde (Fula), Hassaniya Arabic, Mamara (Minyanka), Maninka, Soninke, Sɔõɔy (Songhay), Senara, Tàmàsàyt (Tamasheq) and Xaasongaxanno (Kassonke), faces severe challenges in digital inclusion limiting economic development, educational advancement, and preservation of cultural heritage (Bird, 2020; Nekoto et al., 2020). These languages share in common a penury of language resources needed to train AI and NLP systems which could play a role in lessening the digital divide (Hammarström et al., 2018). This penury extends from severe in the case of a language like Bambara which has very limited resources to catastrophic for languages like Bomu and Bozo with an almost complete absence of language resources. The need for innovative methods for low-resource languages has spawned varied strategies, such as transfer learning, zero-shot learning, and pre-trained models in related languages (Ruder, 2021; Adelani et al., 2022).