The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation
arXiv:2607.03160v1 Announce Type: cross 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
The rapid advancement of LLMs, specifically GPT-5.2, necessitates practical investigations into optimizing their performance for complex, domain-specific tasks like journalistic translation.
This study provides empirical evidence on how prompt engineering, especially using translation theory principles, can significantly enhance LLM output quality for critical applications.
The understanding that specific prompt design, rather than just raw model power, is crucial for achieving high-quality, nuanced output from advanced LLMs in multilingual contexts.
- · Translation service providers
- · Multilingual content platforms
- · LLM developers
- · Prompt engineers
- · Translation services relying purely on generic LLM outputs
- · Companies neglecting prompt optimization
Improved machine translation quality for specific cultural and professional domains.
Increased adoption of LLMs for high-stakes multilingual content generation, potentially reducing costs and turnaround times.
Enhanced global information flow and cross-cultural communication facilitated by more accurate and contextually appropriate AI-driven translations.
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Read at arXiv cs.AI