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Machine Translation (MT) has greatly advanced over the years due to thedevelopments in deep neural networks. However, the emergence of Large LanguageModels (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MTdomain. In this context, we believe that the future of MT is intricately tiedto the capabilities of LLMs. These models not only offer vast linguisticunderstandings but also bring innovative methodologies, such as prompt-basedtechniques, that have the potential to further elevate MT. In this paper, weprovide an overview of the significant enhancements in MT that are influencedby LLMs and advocate for their pivotal role in upcoming MT research andimplementations. We highlight several new MT directions, emphasizing thebenefits of LLMs in scenarios such as Long-Document Translation, StylizedTranslation, and Interactive Translation. Additionally, we address theimportant concern of privacy in LLM-driven MT and suggest essentialprivacy-preserving strategies. By showcasing practical instances, we aim todemonstrate the advantages that LLMs offer, particularly in tasks liketranslating extended documents. We conclude by emphasizing the critical role ofLLMs in guiding the future evolution of MT and offer a roadmap for futureexploration in the sector.

A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models