Large Language Models (LLMs) are rapidly reshaping machine translation (MT), particularly by introducing instruction-following, in-context learning, and preference-based alignment into what has traditionally been a supervised encoder-decoder paradigm. This survey provides a comprehensive and up-to-date overview of how LLMs are being leveraged for MT across data regimes, languages, and application settings. We systematically analyze prompting-based methods, parameter-efficient and full fine-tuning strategies, synthetic data generation, preference-based optimization, and reinforcement learning with human and weakly supervised feedback. Special attention is given to low-resource translation, where we examine the roles of synthetic data quality, diversity, and preference signals, as well as the limitations of current RLHF pipelines. We further review recent advances in Mixture-of-Experts models, MT-focused LLMs, and multilingual alignment, highlighting trade-offs between scalability, specialization, and accessibility. Beyond sentence-level translation, we survey emerging document-level and discourse-aware MT methods with LLMs, showing that most approaches extend sentence-level pipelines through structured context selection, post-editing, or reranking rather than requiring fundamentally new data regimes or architectures. Finally, we discuss LLM-based evaluation, its strengths and biases, and its role alongside learned metrics. Overall, this survey positions LLM-based MT as an evolution of traditional MT systems, where gains increasingly depend on data quality, preference alignment, and context utilization rather than scale alone, and outlines open challenges for building robust, inclusive, and controllable translation systems.

翻译：大型语言模型（LLMs）正在迅速重塑机器翻译（MT）领域，尤其是通过将指令跟随、上下文学习和基于偏好的对齐引入传统上以监督式编码器-解码器为主的范式。本综述全面且及时地概述了LLMs如何在不同数据环境、语言和应用场景中被用于机器翻译。我们系统分析了基于提示的方法、参数高效与全量微调策略、合成数据生成、基于偏好的优化，以及结合人类反馈与弱监督反馈的强化学习。特别关注低资源翻译场景，我们审视了合成数据质量、多样性与偏好信号的作用，以及当前RLHF流程的局限性。我们进一步回顾了混合专家模型、面向机器翻译的专用LLMs以及多语言对齐的最新进展，重点探讨了可扩展性、专业化和可访问性之间的权衡。在句子级翻译之外，我们综述了新兴的基于LLMs的文档级与篇章感知机器翻译方法，指出大多数方法通过结构化上下文选择、后编辑或重排序来扩展句子级流程，而非依赖全新的数据体系或架构。最后，我们讨论了基于LLM的评估方法、其优势与偏差，以及其与学习型指标并存的作用。总体而言，本综述将基于LLM的机器翻译定位为传统机器翻译系统的演进，其中性能提升日益依赖于数据质量、偏好对齐和上下文利用而非单纯规模扩张，并概述了构建稳健、包容且可控的翻译系统所面临的开放挑战。