Low Resourced Language

Low-resource language (LRL) research focuses on developing natural language processing (NLP) techniques for languages lacking extensive digital resources, aiming to bridge the technological gap between well-resourced and under-resourced languages. Current research emphasizes leveraging large language models (LLMs), often through techniques like prompt engineering, transfer learning from related high-resource languages, and the creation of smaller, more efficient models tailored to LRLs. This work is crucial for promoting linguistic diversity in AI, enabling access to technology for underrepresented communities, and advancing our understanding of multilingual NLP.