Artificial Intelligence or Cultural Intelligence?  Investigating the Efficiency of Large Language Models (LLMs) in Idiom Creative Translation Tasks

Asma    Aloui

doi:10.34118/jskp.v6i1.4551

Asma Aloui https://orcid.org/0009-0008-1928-0539

DOI: https://doi.org/10.34118/jskp.v6i1.4551

Keywords: Artificial Intelligence, creative translation, culture-bound references, idioms, Large Language Models.

Abstract

The translation of idioms has always been a challenging task for translators due to their culture-bound references and level of creativity. With the rise of Artificial Intelligence technologies, translators and language instructors are using AI to assist them in different language-related tasks; however, the main concern is whether these AI technologies are efficient in creative translation tasks like rendering cultural subtleties and nuances in idioms. The present paper, which falls into the scope of AI-based translation, aims at investigating the efficiency of Large Language Models in translating idioms from Arabic into English. To attain this aim, two AI models ChatGPT and Microsoft Copilot are tested in the English translation of five commonly-used Standard Arabic idioms. After receiving the same prompts, each model’s output is analyzed to verify correctness and accuracy within Venuti’s foreignization and domestication framework. The findings validate the efficiency of both ChatGPT and Microsoft Copilot in rendering idioms with the former opting for foreignization approach and the latter for a combination of foreignization and domestication approaches. The findings also

pinpoint the potential of AI in creative translation instruction.

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References

Aleedy, M., Alshihri, F., Meshoul, S., Al-Harthi, M., Alramlawi, S., Aldaihani, B., ... & Atwell, E. (2025). Designing AI-powered translation education tools: A framework for parallel sentence generation using SauLTC and LLMs. PeerJ Computer Science, 11, e2788. https://doi.org/10.7717/peerj-cs.2788
Alexandrovna, L. E. & Borisovich, L. D. (2015). Literary translation as a creative activity. Научный результат. Вопросы теоретической и прикладной лингвистики, (1), 63-69.
Anastasiou, D. (2010). Idiom treatment experiments in machine translation. Cambridge Scholars Publishing.
Baker, M. (1992). In other words. A coursebook on translation. Routeledge.
Baziotis, C., Mathur, P., & Hasler, E. (2023). Automatic evaluation and analysis of idioms in neural machine translation. Presented at the Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik. Retrieved from https://aclanthology.org/2023.eacl-main.267/
Benyahia, M. (2024). Examining the Efficiency of Machine Translation in Translating English Idioms used in American Media. Journal of Translation and Language Studies, 5(2), 43-55. https://doi.org/10.48185/jtls.v5i2.1070
Fang, X. (2021). Translation as creative writing practice. New Writing, 18(2), 162-176. https://doi.org/10.1080/14790726.2020.1762665
Gassor, D. R. M. (2024). Artificial Intelligence and Technology Implementation in Teaching Translation in Universities. Bulletin of The Faculty of Languages & Translation, 27(2), 351-374. https://doi.org/10.21608/bflt.2024.399201
Guo, P., Ren, Y., Hu, Y., Li, Y., Zhang, J., Zhang, X., & Huang, H. (2024). Teaching large language models to translate on low-resource languages with textbook prompting. Presented at the Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino. Retrieved from https://aclanthology.org/2024.lrec-main.1362/
Johnsen, M. (2024). Large language models (LLMs). Maria Johnsen.
Kleidermacher, H. C., & Zou, J. (2025). Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers. arXiv preprint arXiv:2502.17882. 1–16. https://doi.org/10.48550/arXiv.2502.17882
Kolhar, M., & Alameen, A. (2021). Artificial Intelligence Based Language Translation Platform. Intelligent Automation & Soft Computing, 28(1). https://doi.org/10.32604/iasc.2021.014995
Kuẞmaul, P. (1991). Creativity in the translation process: Empirical approaches. In K. M. van Leuven-Zwart & T. Naaijkens (Eds.), Translation studies: The state of the art (91-101). Rodopi.
Liu, K., & Afzaal, M. (2021). Artificial intelligence (AI) and translation teaching: A critical perspective on the transformation of education. International journal of educational sciences, 33(1-3), 64-73. https://doi.org/10.31901/24566322.2021/33.1-3.1159
Malenova, E. D. (2018). Creative practices in translation of transmedia projects. Journal of Siberian Federal University Humanities & Social Sciences, 5, 775-786. https://doi.org/10.17516/1997-1370-0269
Newmark, P. (1988). A textbook of translation. Prentice hall.
Newmark, P. (1998). More paragraphs on translation. Multilingual matters.
Venuti, L. (1995). The translator’s invisibility. A history of translation. Routledge.
Zan, C., Ding, L., Shen, L., Zhen, Y., Liu, W., & Tao, D. (2024). Building accurate translation-tailored llms with language aware instruction tuning. arXiv preprint arXiv:2403.14399.
https://doi.org/10.48550/arXiv.2403.14399