The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish–Chinese Journalistic Translation

Haohong Lai, Weijia Li


Abstract
This study examines the influence of prompt language and translation theory-driven prompt design on the quality of Spanish–Chinese editorial translations generated by GPT-5.2. A parallel corpus of four EL PAÍS editorials was translated under 48 experimental conditions (4 prompt types × 3 prompt languages × 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 attributed to the single-reference constraint inherent in automated measures. Subtype analysis indicated that translation theory-driven prompts selectively reduced Awkward style errors, whereas Unidiomatic style errors persisted consistently across conditions. Prompt language exhibited negligible impact under both evaluation paradigms. These results indicate that translation theory-driven prompts are advantageous for language learners seeking high-quality editorial translations and underscore the necessity of human evaluation for accurately assessing LLM translation quality in this domain.
Anthology ID:
2026.eamt-1.57
Volume:
Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
Month:
June
Year:
2026
Address:
Tilburg, The Netherlands
Editors:
Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
883–901
Language:
URL:
https://aclanthology.org/2026.eamt-1.57/
DOI:
Bibkey:
Cite (ACL):
Haohong Lai and Weijia Li. 2026. The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish–Chinese Journalistic Translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 883–901, Tilburg, The Netherlands. European Association for Machine Translation.
Cite (Informal):
The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish–Chinese Journalistic Translation (Lai & Li, EAMT 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.eamt-1.57.pdf