Prompting Strategies Enhance GPT-5’s Chinese-English Legal Translation Quality: Versus DeepL

Abstract

Generative artificial intelligence does perform very well in machine translation, but in the legal scene of English-Chinese translation, controllability and professional reliability are not enough. Legal translation itself is characterized by intensive terminology, complex sentence patterns and strict logical requirements, so the requirements for accuracy and efficiency are extremely high. Because of this, the actual effect of this kind of AI in the legal field has not been fully verified, especially in consistency and coherence at the discourse level. In addition, there is a lack of in-depth research on the extent to which the project can improve the quality of translation. This study compares the performance of ChatGPT-5 and DeepL in English-Chinese legal translation, and examines the differences in the effects of different prompt strategies. Five prompt strategies are designed, from simple to complex, to deal with legal texts. There are three evaluation dimensions : lexical richness, semantic accuracy and discourse coherence, and then the statistical analysis method is used to find out the significant differences. The results show that the prompt design has a great influence on the translation quality. The more structured the prompt is, the more consistent and accurate the output is. In terms of semantic accuracy and coherence, GPT-5 is similar to DeepL. However, under the structured prompt, the lexical richness of GPT-5 is better. On the whole, this study promotes the development of legal machine translation, reveals the different effects of prompt engineering, and shows that structured prompts can improve lexical richness, while the two systems have their own advantages. Based on this, an evidence-based prompt optimization framework is proposed.