Machine translation has come a long way, but even the best systems can still produce errors. Researchers have developed a new large language model (LLM) called xTower, designed to not only correct these errors but also explain why they happened in the first place. xTower builds upon TowerBase, an open-source LLM, and provides free-text explanations for translation errors. Imagine getting a translation that misses the mark; xTower could pinpoint the problematic phrases and tell you exactly why they're wrong, perhaps explaining a cultural nuance or a tricky word sense. This is achieved by using a combination of expert human feedback and an automatic error detection tool. Expert translators tested xTower's explanations and found them helpful for understanding the errors and improving translations. Specifically, in tests translating between English and German, the tool's explanations were praised for their usefulness. The model then uses this explanation in a chain-of-thought process to suggest a corrected translation. Interestingly, even when the initial error detection was wrong, xTower could sometimes still offer helpful feedback by identifying other areas for potential improvement. In experiments across multiple language pairs, xTower significantly improved translation quality compared to other LLMs. But it's not always about replacing the original translation. Sometimes the original is good enough, especially with high-quality MT systems. So, the researchers developed a hybrid approach where xTower only steps in to correct the text when the initial translation quality is low. This clever strategy makes the whole system more efficient. This research is an important step toward not just improving translation quality, but also making AI more transparent and trustworthy. By understanding *why* the machine made a mistake, users can have more confidence in the final result. Future work could focus on generalizing this approach to more languages and integrating it with more sophisticated error-detection tools.