Large language models (LLMs) have shown impressive abilities, yet their multilingual capabilities remain surprisingly underutilized. New research introduces a clever technique called "Transplant" that reveals hidden potential within these models, boosting their performance in non-English languages and even enhancing their understanding of cultural nuances. The secret lies in strategically swapping internal components of the model during the generation process. Imagine the LLM's internal workings like a complex engine. Transplant selectively borrows parts optimized for English and integrates them into the processing of other languages, like adding a turbocharger for multilingual understanding. This cross-lingual exchange of knowledge significantly improves accuracy in tasks like question answering and reasoning across various languages. The research focuses on transplanting "feed-forward" activations, crucial components for storing factual knowledge within LLMs. Interestingly, transplanting these activations only when generating the very first word of a response has ripple effects throughout the entire generation process. This targeted intervention avoids unnecessary modifications, maintaining the stability of the model’s output. Perhaps most surprisingly, Transplant also enhances cultural understanding when answering questions in English. By incorporating activations from other languages, the model gains a more nuanced grasp of cultural contexts. This research sheds light on the untapped potential of existing LLMs. Rather than solely relying on ever-larger models or extensive retraining, strategically transferring internal knowledge within the model itself offers a promising pathway to unlocking true multilingualism and cultural awareness in AI.