Cross Lingual Knowledge

Cross-lingual knowledge transfer aims to leverage knowledge learned from high-resource languages (like English) to improve the performance of natural language processing (NLP) models for low-resource languages. Current research focuses on adapting large language models (LLMs) through techniques like multilingual pre-training, instruction tuning, and cross-lingual adapters, often incorporating translation and multi-task learning strategies. These efforts address the critical need for improved NLP capabilities across diverse languages, impacting applications ranging from toxicity detection and clinical phenotyping to information extraction tasks in various domains. The ultimate goal is to achieve more equitable and universally accessible NLP technology.