Imagine a world where AI seamlessly understands and responds to you, no matter what language you speak. That's the vision driving the exciting new research behind M2Lingual, a project focused on making AI truly multilingual and conversational. The challenge? Most AI models excel in English but struggle with other languages, especially in complex, multi-turn conversations. Existing multilingual datasets often rely on translations, which can miss the nuances of different languages, or are limited to simple, single-turn instructions. Researchers tackled this by creating M2Lingual, a massive dataset of 182,000 instruction-response pairs spanning 70 languages. Instead of simple translations, M2Lingual uses a clever two-step process. First, they gather diverse seed examples in different languages, capturing regional dialects and slang. Then, they use a novel “Evol” taxonomy to transform these seeds into more complex, multi-turn instructions. Think of it like evolving a simple question into a detailed, nuanced conversation. This approach not only expands the linguistic diversity of the dataset but also makes AI better at understanding follow-up questions, changes in context, and even recalling information from earlier parts of a discussion. In tests, models trained with M2Lingual significantly outperformed those using existing multilingual datasets, especially in low-resource languages and multi-turn conversations. What's even more exciting? M2Lingual's benefits are especially pronounced for smaller, more accessible AI models, opening doors for wider use in communities with limited resources. While longer, more complex conversations remain a challenge, M2Lingual represents a crucial leap forward in making AI truly inclusive and universally understood.