Chen Shiyue, Lin Yan
School of Humanities and Foreign Languages, Zhejiang Shuren University, Hangzhou, China.
School of Foreign Languages, Ningxia Normal University, Guyuan, China.
Front Artif Intell. 2025 Jul 24;8:1619489. doi: 10.3389/frai.2025.1619489. eCollection 2025.
This study systematically compares the translation performance of ChatGPT, Google Translate, and DeepL on Chinese tourism texts, focusing on two prompt-engineering strategies. Using a mixed-methods approach that combines quantitative expert assessments with qualitative analysis, the evaluation centers on fidelity, fluency, cultural sensitivity, and persuasiveness. ChatGPT outperformed its counterparts across all metrics, especially when culturally tailored prompts were used. However, it occasionally introduced semantic shifts, highlighting a trade-off between accuracy and rhetorical adaptation. Despite its strong performance, human post-editing remains necessary to ensure semantic precision and professional standards. The study demonstrates ChatGPT's potential in domain-specific translation tasks while calling for continued oversight in culturally nuanced content.
本研究系统地比较了ChatGPT、谷歌翻译和DeepL在中文旅游文本上的翻译表现，重点关注两种提示工程策略。采用将定量专家评估与定性分析相结合的混合方法，评估集中在忠实度、流畅性、文化敏感性和说服力上。ChatGPT在所有指标上都优于其他工具，尤其是在使用针对文化进行调整的提示时。然而，它偶尔会出现语义转换，这凸显了准确性和修辞适应性之间的权衡。尽管表现出色，但仍需要人工后期编辑以确保语义精确性和专业标准。该研究展示了ChatGPT在特定领域翻译任务中的潜力，同时呼吁对文化细微差别内容持续进行监督。
