Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the Sámi Language ...

• Main Authors: Paul, Ronny, Buckchash, Himanshu, Parida, Shantipriya, Prasad, Dilip K.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Computation and Language cs.CL; Artificial Intelligence cs.AI; FOS Computer and information sciences; Sámi
• Online Access: https://dx.doi.org/10.48550/arxiv.2405.05777; https://arxiv.org/abs/2405.05777

Sámi, an indigenous language group comprising multiple languages, faces digital marginalization due to the limited availability of data and sophisticated language models designed for its linguistic intricacies. This work focuses on increasing technological participation for the Sámi language. We draw the attention of the ML community towards the language modeling problem of Ultra Low Resource (ULR) languages. ULR languages are those for which the amount of available textual resources is very low, and the speaker count for them is also very low. ULRLs are also not supported by mainstream Large Language Models (LLMs) like ChatGPT, due to which gathering artificial training data for them becomes even more challenging. Mainstream AI foundational model development has given less attention to this category of languages. Generally, these languages have very few speakers, making it hard to find them. However, it is important to develop foundational models for these ULR languages to promote inclusion and the tangible ...