Can AI understand every language? That’s the dream, but it's tough when some languages have very little data online. Training large language models (LLMs) requires massive datasets, leaving many languages behind. However, a new technique called “model merging” offers a potential solution. Imagine combining the strengths of different AI models – one excellent at English, another familiar with a low-resource language like Mongolian – into a single powerful model. This is the promise of model merging. Researchers explored this idea with fascinating results. They found that merging models is remarkably effective, especially when data is incredibly scarce. Instead of the traditional train-then-fine-tune approach, merging allows these AI to grasp a language's nuances while gaining the ability to perform tasks like translation and question answering. The trick lies in resolving conflicts when combining models. Researchers discovered that as they trained a language model with more data, the merging process started discarding important information from the task-solving model. To address this, they introduced a "slack variable," allowing for more flexibility and preserving key parameters. The findings are encouraging, suggesting that model merging could break down language barriers by leveraging existing AI capabilities, even for languages with limited digital presence. This breakthrough could open doors to a more inclusive AI landscape, allowing speakers of all languages to benefit from technological advancements.