Translation Sample

Translation sample research focuses on improving the accuracy and efficiency of machine translation, particularly for low-resource languages and complex tasks like semantic parsing. Current efforts leverage large language models (LLMs) like PaLM and ByT5, exploring techniques such as few-shot prompting and retrieval-augmented in-context learning to enhance translation quality. This work is significant for expanding access to information across linguistic boundaries and advancing the capabilities of LLMs in handling nuanced linguistic structures and diverse datasets, including the creation of synthetic multilingual corpora for training and evaluation.