Shot Translation

Shot translation, particularly few-shot and zero-shot translation, focuses on enabling machine translation with minimal parallel training data, leveraging the capabilities of large language models (LLMs) and multilingual models. Current research emphasizes improving performance by techniques like style matching, data augmentation, and prompt engineering, often incorporating methods such as knowledge distillation and manifold deformation. These advancements are significant for extending machine translation to low-resource and under-represented languages, impacting both linguistic research and practical applications like cross-lingual communication and information access.