Target Translation

Target translation, the process of accurately converting text or images from one language or modality to another, faces challenges like "off-target" translations (incorrect language output) and hallucinations (fabricated content). Current research focuses on improving large language models (LLMs) and other architectures by refining training methods, such as incorporating contrastive learning and instruction tuning, to enhance instruction following and reduce errors. These advancements aim to improve the accuracy and reliability of machine translation, particularly for low-resource languages, impacting fields like multilingual communication and cross-cultural understanding.