General Translation Task

General machine translation (GMT) research focuses on developing systems capable of translating between numerous language pairs using a single model, addressing the challenges of multilingualism and low-resource languages. Current efforts leverage large language models (LLMs) and transformer-based architectures, often employing techniques like multilingual pre-training followed by fine-tuning on parallel data, and exploring methods to improve translation quality, fluency, and elegance, particularly for challenging domains like classical poetry. These advancements are significantly impacting the field by improving the efficiency and effectiveness of machine translation across diverse languages and contexts, though robustness against adversarial attacks remains an active area of investigation.