Optimizing Prompt Engineering in Translation Practice: A Comparative Study of ChatGPT-4.0 and ChatGPT-4.o Mini
Abstract
Recent advancements in natural language processing (NLP) have introduced large language models (LLMs) like ChatGPT-4.0 and its smaller variant, ChatGPT-4o mini, which are increasingly utilized in machine-aided translation (MT). This study investigates the critical role of prompt engineering in enhancing translation quality using these models. By conducting a systematic comparison between ChatGPT-4.0 and ChatGPT-4o mini, we examine how different prompt designs influence translation accuracy and fluency across various text types. Through rigorous literature review, empirical analysis, and comprehensive sample evaluation, this research provides an in-depth assessment of how prompt engineering affects translation outputs. The findings offer practical recommendations for improving translation practices and set the stage for future research in this evolving domain.
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DOI: http://dx.doi.org/10.3968/13573
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