Multilingual Instruction

Multilingual instruction tuning aims to enhance large language models (LLMs) to effectively follow instructions across diverse languages, addressing the performance gap between English and low-resource languages. Current research focuses on developing efficient methods for creating multilingual instruction datasets, often leveraging techniques like reverse instruction generation and cross-lingual transfer learning, and employing model architectures such as mT5 and various parameter-efficient fine-tuning methods. This work is significant because it strives to make LLMs more equitable and accessible globally, impacting both scientific understanding of cross-lingual transfer and the practical application of LLMs in multilingual contexts.