Imperfect Translation

Imperfect translation in machine translation (MT) systems, despite advancements, remains a significant challenge hindering reliable application. Current research focuses on improving translation accuracy by identifying and correcting errors through methods like large language models (LLMs) that offer explanations and suggest corrections, algorithms that filter or edit noisy training datasets, and novel testing approaches to detect errors more effectively. These efforts aim to enhance the quality and trustworthiness of MT outputs, impacting various fields where accurate cross-lingual communication is crucial, such as healthcare and finance.