Advances in Machine Translation: A Comprehensive Survey of Large Language Models

The conventional line of machine translation (MT) has been revolutionized by the advent of large language models (LLMs) that have dramatically increased translation performance on multi languages. In this survey, we conduct a thorough analysis of how and why LLMs have both developed and been applied to the problem of machine translation. The paper investigates these basic models including Transformer, mBART, and GPT, as well as recent advances such as zero shot and few shot learning capabilities. Multilingual translation, domain adaptation and dealing with low resource languages are all given special treatment. We discuss key challenges, like scalability, model interpretability, and bias mitigation, and potential solutions. The survey also sheds light on how MT is merging with emotion detection, in real time translation and surprisingly, integrating multimodal inputs from humans. In general, this work seeks to provide a view into the current landscape concerning opportunities and potential future directions with which LLMs can augment existing machine translation systems.