The advent of Large Language Models (LLMs) has significantly reshaped the landscape of machine translation (MT), particularly for low-resource languages and domains that lack sufficient parallel corpora, linguistic tools, and computational infrastructure. This survey presents a comprehensive overview of recent progress in leveraging LLMs for MT. We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning that enable effective adaptation to under-resourced settings. The paper also explores synthetic data generation strategies using LLMs, including back-translation and lexical augmentation. Additionally, we compare LLM-based translation with traditional encoder-decoder models across diverse language pairs, highlighting the strengths and limitations of each. We discuss persistent challenges such as hallucinations, evaluation inconsistencies, and inherited biases while also evaluating emerging LLM-driven metrics for translation quality. This survey offers practical insights and outlines future directions for building robust, inclusive, and scalable MT systems in the era of large-scale generative models.

本研究针对低资源语言和缺乏足够并行语料库的领域，探讨了如何利用大型语言模型（LLMs）改善机器翻译的有效性。通过分析少量示例提示、跨语言迁移和高效微调等技术，研究提出了适应资源匮乏环境的新方法。同时，比较了基于LLM的翻译与传统编码-解码模型，指出每种方法的优缺点及未来建设更强大、包容和可扩展的机器翻译系统的方向。

弥合语言鸿沟：利用大型语言模型进行机器翻译的综述