Imagine an AI translator that can seamlessly adapt to the nuances of different languages, unlocking new levels of accuracy. This isn't science fiction; researchers are pushing the boundaries of machine translation with an innovative two-phase training method for Large Language Models (LLMs). The first phase focuses on continual pre-training using diverse parallel datasets, essentially immersing the model in the intricate dance between source and target languages. This process allows the model to grasp the subtle relationships between words and phrases, much like a language student absorbing grammar and vocabulary. The second phase refines this raw understanding with supervised fine-tuning on smaller, high-quality parallel data, polishing the translation output to near-human levels of fluency. This two-step process is proving particularly effective in boosting translation accuracy for LLMs, outperforming conventional encoder-decoder models on a variety of tasks. Notably, the research reveals how LLMs benefit from training on data where source and target sentences are interleaved, allowing them to decipher and retain knowledge about translation direction. This is akin to learning a language by studying conversational exchanges, where sentences flow naturally between languages. This approach enables the model to not only translate accurately but also to adapt its tone and style to match the original text. But there's more to this story than simply switching training methods. The quality and quantity of data used in the pre-training phase are crucial. This research delves into the different ways parallel data can be formatted, experimenting with prefixes, tags, and even JSON structures to guide the model's learning. The results show that adding explicit markers about the target language's direction enhances the model's ability to learn and generalize. The implications of this research are substantial. Imagine an LLM-powered translation tool that can not just translate documents but also adapt to the nuances of spoken language, capturing the subtleties of conversations and presentations. The ability to train with smaller high-quality datasets also democratizes access to this technology, potentially enabling translation models for under-resourced languages. While the current research focuses primarily on English-Japanese and Japanese-English translation, the findings pave the way for exploring this technique across a multitude of languages. This two-phase training approach, with its focus on continual learning, represents a crucial step towards universally accurate and adaptable machine translation.