Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability
This work addresses the problem of improving machine translation customization for translators and industries, but it is incremental as it builds on existing prompt engineering and translation practices.
The study investigated how incorporating translation purpose and target audience into prompts affects ChatGPT's translation quality, finding that such prompts can enhance translation quality according to industry standards, particularly for marketing documents and idioms.
This paper explores the influence of integrating the purpose of the translation and the target audience into prompts on the quality of translations produced by ChatGPT. Drawing on previous translation studies, industry practices, and ISO standards, the research underscores the significance of the pre-production phase in the translation process. The study reveals that the inclusion of suitable prompts in large-scale language models like ChatGPT can yield flexible translations, a feat yet to be realized by conventional Machine Translation (MT). The research scrutinizes the changes in translation quality when prompts are used to generate translations that meet specific conditions. The evaluation is conducted from a practicing translator's viewpoint, both subjectively and qualitatively, supplemented by the use of OpenAI's word embedding API for cosine similarity calculations. The findings suggest that the integration of the purpose and target audience into prompts can indeed modify the generated translations, generally enhancing the translation quality by industry standards. The study also demonstrates the practical application of the "good translation" concept, particularly in the context of marketing documents and culturally dependent idioms.