How different prompts affect GPT-5's Chinese-to-English translation performance of government work reports

In recent years, the rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-5 have ushered machine translation into a new era. This study examines the impact of different prompts - simple prompts, complex prompts, and few-shot prompts - on GPT-5s translation performance for the 2024 Chinese Government Work Report, finding that while complex prompts yielded better results in automatic evaluation metrics, human assessment showed no substantial differences in translation quality between simple and complex prompts. The few-shot prompting approach displayed potential in adapting to the text style, but still faced common machine translation challenges, underscoring the importance of thoroughly analyzing text requirements and providing targeted prompt instructions when utilizing large language models for translation, as well as the need for future translators to master the characteristics of these models and develop the ability to identify and adjust translation issues, in order to enhance the practical effectiveness of machine translation.