Abstract: This study delves into the core principles and applications of generative large language models (LLMs) in translation technology, analyzing how these models achieve high-precision and high-efficiency automatic translation with their superior language generation capabilities and deep learning architecture. By examining the structure, training methods, and optimization strategies of generative LLMs, we uncover their mechanisms in handling language mapping, contextual information, and complex linguistic phenomena. The research demonstrates that appropriate model selection and adjustment can significantly improve translation accuracy and fluency, catering to diverse translation needs. Additionally, this study emphasizes the importance of establishing a scientific translation quality evaluation system for ongoing improvements in translation quality. We also address the challenges current research faces, such as corpus quality, model complexity, and computational resource consumption, and propose future research directions, including expanding corpus resources, optimizing model structures, integrating other natural language processing technologies, and enhancing multilingual and cross-domain translation research.

Keywords: Generative Large Language Model; Translation principles; Deep learning; Translation quality; Natural language processing; Corpus expansion; Model optimization.

Title: Research on the Translation Principles Based on Generative Large Language Models

Author: GOU NYUHUAN

International Journal of Novel Research in Interdisciplinary Studies

ISSN 2394-9716

Vol. 11, Issue 4, July 2024 - August 2024

Page No: 1-8

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 31-July-2024