Cross Lingual

Cross-lingual research focuses on bridging language barriers in natural language processing, aiming to build models that understand and process text across multiple languages. Current efforts concentrate on improving multilingual large language models (LLMs) through techniques like continual pre-training, adapter modules, and contrastive learning, often addressing challenges related to low-resource languages and semantic alignment. This field is crucial for expanding access to NLP technologies globally and enabling cross-cultural communication and information exchange in diverse applications, such as machine translation, sentiment analysis, and cross-lingual information retrieval.