Journal of Basic and Applied Research International

This paper systematically explores the technical characteristics of large language models (LLMs) and their transformative role in translation practice and project management. Built on the Transformer architecture and trained on massive textual corpora, LLMs exhibit generative, interactive, and multifunctional capabilities, enabling breakthroughs in translation tasks. The study first defines LLMs as deep learning models based on extensive text training and Transformer architecture, capable of performing diverse tasks such as translation and text generation. It then analyzes the application value of LLMs in translation practice, including improvements in translation quality, support for multimodal translation, and the intelligent upgrading of computer-assisted translation (CAT) tools. Furthermore, the paper outlines the traditional workflow of translation project management—initiation, planning, execution, monitoring, and closure—and highlights the transformative impact of LLMs on key stages, particularly initiation, execution, and revision. Finally, it identifies future trends, such as the evolution of project management toward intelligence and collaboration, and the expansion of quality assessment criteria to include cultural adaptability and user experience. The study also addresses emerging challenges, including ethical risks, the need for translator skill transformation, and semantic biases in model outputs. To foster deep integration of human-machine collaboration, future efforts should focus on technological optimization, ethical standardization, and translator training.