Multilingual Model XLM

Multilingual models, such as XLM and its variants (e.g., XLM-R, XGLM), aim to process and understand multiple languages simultaneously, overcoming limitations of monolingual models. Current research focuses on improving their performance in low-resource languages, exploring techniques like data augmentation (e.g., using parallel examples from grammar books) and model compression (e.g., quantization) to enhance efficiency and generalization. These advancements are significant for broadening the reach of natural language processing to a wider range of languages and applications, including cross-lingual information retrieval, sentiment analysis, and named entity recognition.