Bilingual Model

Bilingual language models aim to achieve high performance in two languages simultaneously, often leveraging techniques like multi-task learning and contrastive learning to improve efficiency and cross-lingual transfer. Current research focuses on developing larger bilingual models (e.g., using transformer architectures) trained on massive datasets, exploring innovative training strategies (e.g., curriculum learning, auxiliary losses), and mitigating biases inherent in multilingual data. These advancements are significant for expanding access to natural language processing capabilities for under-resourced languages and improving cross-lingual applications in diverse fields like machine translation, question answering, and biomedical text analysis.