Multilingual Pre Trained Language Model

Multilingual pre-trained language models (mPLMs) aim to build language understanding capabilities across many languages simultaneously, leveraging massive multilingual datasets for training. Current research focuses on improving cross-lingual transfer, particularly for low-resource languages, through techniques like prompt engineering, data augmentation (including transliteration), and model adaptation methods such as adapter modules and knowledge distillation. These advancements are significant because they enable more efficient and effective natural language processing applications across a wider range of languages, impacting fields like machine translation, information retrieval, and cross-lingual understanding.