The development of operational multilingual Large Language Models (LLMs) typically involves resource-intensive stages of pretraining, instruction tuning, and alignment. Crucially, high-quality instruction and preference datasets are essential for effective alignment, yet their creation necessitates substantial human labor for each target language, posing a significant barrier to inclusivity and democratization of AI, especially for languages beyond English.
