Multilingual Capability

Multilingual capability in large language models (LLMs) focuses on developing models that perform well across many languages, addressing the current dominance of English-centric systems. Research actively explores techniques like multilingual instruction tuning, continual pre-training, and manipulation of internal language representations to improve performance, particularly for low-resource languages, while mitigating issues like catastrophic forgetting and bias. This field is crucial for broadening AI accessibility globally and fostering equitable access to advanced AI services, impacting both scientific understanding of language representation and the development of inclusive real-world applications.