Translation Model

Translation models aim to automatically convert text from one language to another, focusing on improving accuracy, efficiency, and robustness. Current research emphasizes leveraging large language models (LLMs) and neural machine translation (NMT) architectures, exploring techniques like knowledge distillation, data augmentation (including synthetic data generation), and fine-tuning strategies to address challenges such as low-resource languages, idiom translation, and hallucination. These advancements have significant implications for cross-lingual communication, information access, and the development of multilingual applications across various domains, including healthcare and social media.