The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation
Summary
This study investigates how prompt language and translation-theory-driven prompt design affect the quality of Spanish-Chinese journalistic translations produced by GPT-5.2, finding that theory-driven prompts improve style under expert evaluation despite automated metrics favoring baseline prompts.
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# The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation Source: [https://arxiv.org/abs/2607.03160](https://arxiv.org/abs/2607.03160) [View PDF](https://arxiv.org/pdf/2607.03160) > Abstract:This study examines how prompt language and translation theory\-driven prompt design influence the quality of Spanish\-Chinese journalistic translations generated by GPT\-5\.2\. A parallel corpus of four editorials from El Pais was translated under 48 experimental conditions \(4 prompt types, 3 prompt languages, and 4 articles\)\. Translation quality was assessed using BLEU and BERTScore\-F1 for automated evaluation, alongside human evaluation based on the Multidimensional Quality Metrics \(MQM\) framework\. Automated metrics identified the baseline prompt \(BASE\) as the best\-performing condition, whereas human evaluation ranked the brief\-oriented prompt \(BRIEF\) highest \(MQM: 8\.66 vs\. 7\.84\), a reversal likely attributable to the single\-reference constraint inherent in automated measures\. Sub\-error type analysis revealed that translation theory\-driven prompts selectively reduced Awkward style errors, while Unidiomatic style errors persisted across conditions\. Prompt language had a negligible impact under both evaluation paradigms\. These results indicate that translation theory\-driven prompts can yield measurable quality gains under expert evaluation of journalistic translations, although their pedagogical implications for language learners remain suggestive and require validation through user\-based studies\. ## Submission history From: Haohong Lai \[[view email](https://arxiv.org/show-email/48086053/2607.03160)\] **\[v1\]**Fri, 3 Jul 2026 09:59:36 UTC \(508 KB\)
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