Discover how innovations in LLMs are revolutionizing support for low-resource languages, bridging linguistic gaps, and fostering inclusivity in AI inclusivity.
Low Resource Languages in Large Language Models In recent years, the emergence of Large Language Models has brought about significant shifts in the daily routines of consumers. Individuals can now undertake a diverse range of tasks, such as retrieving information, composing text, and refining documents through these powerful language tools. This integration of LLMs into daily life has resulted in notable boosts in productivity, both at work and in personal endeavors.
Innovative pipeline to gather data for LRLs Swahili is a language spoken by over 200 million people across 14 different African countries and is the official national language in Tanzania, Kenya, Uganda, and the Democratic Republic of the Congo. It belongs to the group of low-resource languages and is an example of a language that does not have an out-of-the-box instruction dataset for LLM fine-tuning. In general, three approaches exist to create a fine-tuning dataset for a language.
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