Multilingual Training Data

Multilingual training data focuses on developing machine learning models capable of understanding and processing multiple languages, particularly addressing the challenges posed by low-resource languages. Current research emphasizes efficient training methods, such as parameter-efficient fine-tuning and advanced embedding techniques, often applied to transformer-based models to improve performance without requiring massive datasets for each language. This research is crucial for broadening access to natural language processing technologies globally and enabling cross-lingual applications in diverse fields like information retrieval, sentiment analysis, and software engineering.