Imagine a world where language barriers in AI are a distant memory. A world where large language models (LLMs) can seamlessly switch between languages, understanding and generating text with native-level fluency. This dream is moving closer to reality thanks to new research focusing on improving how LLMs learn to follow instructions in multiple languages. Historically, creating multilingual instruction datasets has been a double-edged sword. Directly translating English instructions often resulted in awkward, unnatural phrasing and failed to capture the nuances of different languages. Templating—creating instructions based on pre-defined structures—often led to repetitive prompts, limiting the diversity of instructions that LLMs were exposed to during training. This new research proposes an innovative method that addresses these limitations. Instead of translating instructions or relying on templates, this method leverages the power of existing English LLMs to generate instructions directly from non-English responses. This approach begins by selecting a diverse range of text fragments from monolingual sources. These fragments are then translated into English and fed to an English LLM, which generates corresponding instructions. A scoring function ensures that the generated instruction-response pairs are high-quality and well-aligned. Finally, the English instructions are translated back into the original language, creating a dataset rich in linguistic diversity and naturalness. The results are impressive. LLMs trained on these new multilingual datasets significantly outperformed models trained using traditional translation or templated datasets on both generative tasks like summarization and translation, and discriminative tasks requiring language comprehension. This research not only reveals a more effective way to train multilingual LLMs but also highlights the importance of linguistic diversity in AI. By preserving the nuances of each language, we can unlock the full potential of AI, creating models that can truly understand and engage with the world in all its linguistic richness. While this research primarily focused on a few languages, the potential impact is global. This new method paves the way for training LLMs that can seamlessly switch between hundreds, even thousands, of languages, opening doors to improved cross-cultural communication, more accurate translation services, and more personalized language learning experiences. The future of multilingual AI is bright, and with continued research in this direction, we can expect to see LLMs that are not just proficient in multiple languages, but truly multilingual in their understanding and communication.