Multilingual Representation

Multilingual representation research aims to create computational models that effectively understand and process multiple languages, overcoming limitations of monolingual approaches. Current efforts focus on developing and improving multilingual large language models (MLLMs), often employing techniques like dual encoders, knowledge graph embeddings, and various alignment methods (e.g., density matching) to enhance cross-lingual transfer and reduce performance disparities between high- and low-resource languages. These advancements are significant for broadening access to natural language processing tools and fostering cross-cultural communication and understanding in diverse applications, including machine translation, cross-lingual information retrieval, and clinical text analysis.