Optimizing Prompt Engineering in Translation Practice: A Comparative Study of ChatGPT-4.0 and ChatGPT-4.o Mini

Wei WANG, Weihong ZHOU

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.


Keywords


Prompt engineering; Machine-aided translation; ChatGPT-4.0; ChatGPT-4o Mini; Natural language processing; Translation quality; Language models

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References


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DOI: http://dx.doi.org/10.3968/13573

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