Multilingual Ability

Multilingual ability in large language models (LLMs) is a burgeoning research area focused on understanding and improving how these models process and generate text across multiple languages. Current research investigates methods to enhance multilingual capabilities, including techniques like model merging, weight disentanglement, and adapting existing models to new languages through methods such as LoRA. This work is crucial for broadening the accessibility and applicability of LLMs, ensuring equitable performance across diverse linguistic communities and facilitating cross-lingual communication and information access.