Optimizing ChatGPT for Enhanced Machine Translation: A Systematic Approach to Contextual Accuracy and Cross-Lingual Consistency

Abstract

multilingual interactions across various digital platforms. Recent advancements in large language models (LLMs) like ChatGPT offer significant potential for improving MT quality by providing a better understanding of contextual and nuanced language. However, maintaining contextual accuracy and cross-lingual consistency across translations remains a challenging task, particularly in real-time applications. This paper presents a systematic approach to optimizing ChatGPT for machine translation, focusing on techniques to enhance contextual understanding, reduce ambiguity, and ensure consistent translation across multiple languages. By investigating factors such as context retention, idiomatic expressions, semantic fidelity, and user interaction, we propose a framework for fine-tuning ChatGPT to meet the demands of accurate and reliable MT. Our approach demonstrates improvements in translation fluency and accuracy, offering insights into the practical and theoretical implications of using LLMs for cross-lingual applications. This paper provides a comprehensive analysis of the challenges and proposes methodologies for leveraging ChatGPT’s capabilities to develop a robust MT system.