AWEJ for Translation & Literary Studies, Volume 9. Number 2. May 2025 Pp.139-153
Department of Languages and Literature
College of Humanities and Social Sciences
United Arab Emirates University
Al Ain, UAE
Unlike conventional machine translation (MT), large language models (LLMs) are unique in producing various translations based on different prompts. Researchers have suggested designing detailed translation prompts to improve the translation output of artificial intelligence (AI)-powered LLMs. This study investigates how LLMs respond to prompts that include extratextual information typically included in a translation brief to a human translator. The underlying goal is to probe LLM models’ potential in human translator work and training. To answer the research question, three passages from tourist brochures were submitted to ChatGPT with five prompts, which included information on the text function, translation purpose, and target audience. To identify any effect on the output, cultural references and promotional devices (e.g., imagery, personal deixis, and emotive words) were identified in the passages, followed by a comparison of their translations across the five texts. The study’s significance lies in applying concepts in translation theories such as the translation brief to the novel field of prompt design, moving beyond the usual linguistic accuracy metrics of MT assessment. The analysis showed that ChatGPT did not recognize cultural references as elements that might require explication for the target audience. It also rendered textual persuasive devices faithfully regardless of any changes in the prompts. These faithful translations were not always successful in preserving the textual functions of persuasion. The results suggest that more specific and detailed prompts are needed to bring MT closer to human translation.
Sharkas, H. (2025). Exploring the Role of Translation Brief Elements in Prompts to Large Language Models. Arab World English Journal for Translation & Literary Studies 9 (2): 139-153
Ahmed, I., Kajol, M., Hasan, U., Datta, P. P., Roy, A., & Reza, M. R. (2024). ChatGPT versus Bard: A comparative study. Engineering Reports, 6, (11), 1-18. https://doi.org/10.1002/eng2.12890
Al-Batineh, M., & Rabadi, R.I. (2019). Will the machine understand literary translation? A glimpse into the future of literary machine translation through the lenses of artificial intelligence, Studies in Translation, 5, (1), 151-169. Retrieved December 27, 2023 from https://www.researchgate.net/publication/337008364
Aldawsari, H. A. H. (2024). Evaluating translation tools: Google Translate, Bing Translator, and Bing AI on Arabic colloquialisms. Arab World English Journal, Special Issue on ChatGPT, April 2024, 237–251. https://doi.org/10.24093/awej/chatgpt.16
Alosaimi, B. A., & Alawad, N. A. (2024). Evaluation of the translation of separable phrasal verbs generated by ChatGPT. Arab World English Journal, Special Issue on ChatGPT, April 2024, 282–291. https://doi.org/10.24093/awej/chatgpt.19
Al Rousan, R. A., Jaradat, R., & Malkawi, M. (2025). ChatGPT translation vs. human translation: an examination of a literary text. Cogent Social Sciences, 11, (1), 1-21. https://doi.org/10.1080/23311886.2025.2472916
Banimelhem, O., & Amayreh, W. (2023). Is ChatGPT a good English to Arabic machine translation tool? In 2023 14th International Conference on Information and Communication Systems (ICICS) (pp. 21-23). IEEE. https://doi.org/10.1109/ICICS60529.2023.10330525
DAIR.AI. General Tips for Designing Prompts. (2024). https://www.promptingguide.ai/introduction/tips
Gao, Y., Wang, R., & Hou, F. (2024). How to Design Translation Prompts for ChatGPT: An Empirical Study. In, R. Wang, Z. Wang, J. Liu, A. del Bimbo, J. Zhou, A. Basu & M. Xu (Eds.), MMASIA ’24 Workshops: Proceedings of the 6th ACM International Conference on Multimedia in Asia Workshops (pp.1-7). Association for Computing Machinery. https://doi.org/10.1145/3700410.3702123
Hadi, M. U., Al Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Anas Zafar, A. Shaikh, M.B. Akhtar, N., Hassan, S.Z., Shoman M., Wu, J., Mirjalili, S., & Shah, M. (2025). Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects, TechRxiv, 8, 1-55. https://doi.org/10.36227/techrxiv.23589741.v8
He, S. (2024). Prompting ChatGPT for translation: A comparative analysis of translation brief and persona prompts. In C. Scarton, C. Prescott, C. Bayliss, C. Oakley, J. Wright, S. Wrigley, X. Song, E. Gow-Smith, R. Bawden, V. M. Sánchez-Cartagena, P. Cadwell, E. Lapshinova-Koltunski, V. Cabarrão, K. Chatzitheodorou, M. Nurminen, D. Kanojia & H. Moniz (Eds.), Proceedings of the 25th annual conference of the European Association for Machine Translation (Volume 1) (pp. 316–326). European Association for Machine Translation. Retrieved May 22, 2025 from https://aclanthology.org/2024.eamt-1.27.pdf
He, Z., Tian, L., Jiao, W., Zhang, Z., Yang, Y., Wang, R., Tu, Z., Shi, S., & Wang, X. (2024, March 08). Exploring Human-Like Translation Strategy with Large Language Models, Transactions of the Association for Computational Linguistics, 12, 229–246. https://doi.org/10.1162/tacl_a_00642
Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y. J., Afify, M., & Awadalla, H. H. (2023). How good are GPT models at machine Translation? A comprehensive evaluation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2302.09210
Introduction to Prompt Design. Google Cloud. (2023). Retrieved August 08, 2023 from https://cloud.google.com/vertex-ai/docs/generative-ai/learn/introduction-prompt-design#what-is-a-prompt
Jiao, W., Wang, W., Huang, J.T., Wang, X., Shi, S., & Tu, Zhaopeng. (2023). Is ChatGPT a good translator? A preliminary study, arXiv preprint arXiv:2301.08745. Retrieved July 09, 2023 from https://arxiv.org/pdf/2301.08745.pdf
Jiao, H, Peng, B, Zong, L, Zhang, XJ, & Li, XW (2024). Gradable ChatGPT Translation Evaluation. Procesamiento del Lenguaje Natural, 72, 73-8. https://doi.org/10.26342/2024-72-5
Jurafsky, D., & Martin, J.H. (2023). Speech and language processing (3rd ed. draft). Retrieved August 10, 2023 https://web.stanford.edu/~jurafsky/slp3/ed3book_jan72023.pdf
Kit, C., & Wong, B.M.T. (2023) Evaluation in machine translation and computer-aided translation In C. San Wai (ed.), Routledge Encyclopedia of Translation Technology (pp. 219-244). London: Routledge. https://doi.org/10.4324/9781003168348-13
Lyu, C., Xu, J., & Wang, L. (2023). New trends in machine translation using large language models: case examples with ChatGPT, arXiv preprint arXiv:2305.01181. Retrieved July 09, 2023 from https://arxiv.org/pdf/2305.01181.pdf.
Nord, C. (1997). Translating as a purposeful activity. Functionalist approaches explained. Manchester: St Jerome.
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y. & Tao, M. (2023). Towards Making the Most of ChatGPT for Machine Translation. In H. Bouamor, J. Pino & K. Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 5622–5633). Association for Computational Linguistics. Retrieved May 22, 2025 from https://aclanthology.org/2023.findings-emnlp.373.pdf
Schade, M. (2023). How ChatGPT and our language models are developed, OpenAI. Retrieved August 13, 2023 from https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed
Siu, S.C. (2024). Revolutionising Translation with AI: Unravelling Neural Machine Translation and Generative Pre-trained Large Language Models. In Y. Peng, H. Huang, & D. Li (Eds.), New advances in translation technology. Applications and pedagogy (pp. 29-54). Springer. https://doi.org/10.1007/978-981-97-2958-6_3
Torres-Hostench, O. (2022). Will translators be cyborgs? What would make a cyborg translator?, Revista Tradumàtica, 20, 268–275. https://doi.org/10.5565/rev/tradumatica.316
Torresi, I. (2021). Translating promotional and advertising texts (2nd ed.) London and New York: Routledge.
Wang, H., Hua, W., He, Z., Huang, L. & Church, K. W. (2022). Progress in machine translation, Engineering, 18, 143-153. https://doi.org/10.1016/j.eng.2021.03.023.
Wang, L. (2023). The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals. SHS Web of Conferences, 163, 02021. https://doi.org/10.1051/shsconf/202316302021
Yamada, M. (2023). Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT’s Customizability. In M. Yamada & F. do Carmo (eds.), Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track (pp. 195–204). Asia-Pacific Association for Machine Translation. Retrieved May 22, 2025 from https://aclanthology.org/2023.mtsummit-users.19/
Hala Sharkas is an Associate Professor at UAEU, holding MA and PhD in Translation Studies from the University of Portsmouth, UK. Her research focuses mainly on specialized translation such as scientific and technical translation, health communication, and news translation. She has been teaching both general and specialized translation courses at the graduate and undergraduate levels.
ORCID ID: https://orcid.org/0000-0003-3860-5454