Lingual Alignment

Lingual alignment in natural language processing focuses on enabling language models to effectively share knowledge and perform tasks across multiple languages, overcoming limitations of primarily English-centric training data. Current research emphasizes improving cross-lingual transfer by employing techniques like contrastive learning, modular training architectures that separate monolingual specialization from cross-lingual alignment, and pre-training strategies that establish multilingual alignment early in the model development process. These advancements are crucial for bridging the language gap in various applications, particularly benefiting low-resource languages and promoting the development of truly multilingual AI systems that are safer and more equitable.