ADAPTING LLMs FOR LOW RESOURCE LANGUAGES-TECHNIQUES AND ETHICAL CONSIDERATIONS

Adaptive large language models (LLMs) to resource-scarce languages and also analyze the ethical considerations involved. Already incorporated the elements of mixed methods. It consists of a literature review, corpus collection, expert interviews, and shareholders meeting. Some adaptation techniques examined in this study are data augmentation, multilingual pre-training, change of architecture, and parameter-efficient fine-tuning. The quantitative analysis indicated model performance improvements for under-resourced languages, particularly through cross-lingual knowledge transfer and data augmentation. However, results were varied in terms of languages and tasks. There were ethical issues in qualitative analysis: This articulated an ethical framework around the aspects of inclusive and transparency with the involvement of constituencies along all the lines of bias, cultural sensitivity, privacy of data, and impacts on linguistic diversity. Finally, although transfer learning and data augmentation speak nicely to adapting LLMs toward low-resource languages, very careful consideration must still be given to implications to ensure their fair and contextually appropriate use. Keywords- Adaptive large language models (LLMs), Resource-scarce languages, Data augmentation, Multilingual pre training, Cross lingual knowledge transfer, Ethical consideration, Cultural sensitivity