High Resource Language

High-resource language (HRL) research focuses on addressing the significant performance gap between natural language processing (NLP) models trained on dominant languages (like English and Chinese) and those trained on low-resource languages (LRLs). Current research emphasizes developing and adapting large language models (LLMs), often employing techniques like multilingual fine-tuning, transfer learning from HRLs, and data augmentation strategies to improve performance on LRLs across various tasks such as machine translation, question answering, and sentiment analysis. This work is crucial for promoting linguistic diversity and inclusivity in AI, ensuring equitable access to advanced language technologies for all speakers globally.