An Exploration of AI Applications in Translation in the Context of Large Language Models

With the continuous development of large language models (such as GPT, BERT, etc.), artificial intelligence (AI) has made significant progress in the field of translation. This paper explores the current status and future development of AI in translation under the background of large language models. First, it briefly reviews the technical principles of large language models and their foundational role in natural language processing (NLP), particularly their groundbreaking impact on translation. Next, it analyzes translation technologies based on large language models, including neural machine translation (NMT) and Transformer models, highlighting their advantages in improving translation accuracy, contextual understanding, and long text handling. Additionally, this paper discusses the challenges faced by AI translation, such as cultural differences, translation of lowresource languages, and data privacy issues, and looks forward to future trends, including deep learning, cross-modal translation, and personalized customization. Finally, the paper summarizes the tremendous potential of large language models in advancing translation technology and offers relevant suggestions for future research.