Multilingual Language Model
Multilingual language models (MLLMs) aim to create AI systems capable of understanding and generating text across multiple languages, overcoming the limitations of English-centric models. Current research focuses on improving MLLM performance in low-resource languages, mitigating biases towards dominant languages, and developing techniques for efficient knowledge editing and unlearning to address privacy and ethical concerns. These advancements are crucial for broadening access to AI-powered tools and fostering more equitable and inclusive natural language processing applications globally.
Papers
Scaling Laws for Multilingual Language Models
Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song
Tokenization and Morphology in Multilingual Language Models: A~Comparative Analysis of mT5 and ByT5
Thao Anh Dang, Limor Raviv, Lukas Galke
Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
Chengzhi Zhong, Fei Cheng, Qianying Liu, Junfeng Jiang, Zhen Wan, Chenhui Chu, Yugo Murawaki, Sadao Kurohashi
Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval
Adel Elmahdy, Sheng-Chieh Lin, Amin Ahmad