Impact of Large Language Model on the Translation Profession: Opportunities and Challenges for Human Translators

Authors

  • Charit Senapa English Program, Faculty of Humanities and Social Sciences, Uttaradit Rajabhat University
  • Pannatee Sattayaporn English Program, Faculty of Humanities and Social Sciences, Uttaradit Rajabhat University
  • Lalita Samrittitanon English Program, Faculty of Humanities and Social Sciences, Uttaradit Rajabhat University

Keywords:

Large Language Model, AI translation, translation quality, human translators, natural language processing

Abstract

This study examines the role of Large Language Model (LLM) AI in translation, focusing on its capabilities and the potential risks it poses to human translators. LLM, a form of artificial intelligence trained on massive datasets across multiple languages, has shown significant proficiency in natural language processing and translation tasks. This has led to increased reliance on LLM for translation across various professions, raising concerns about the displacement of human translators. The study investigates the advantages and disadvantages of LLM in translating texts between Thai and English comparing to human translators. Despite the AI's remarkable speed and ability to handle over 100 languages, challenges remain in ensuring cultural appropriateness, linguistic consistency, and contextual understanding. Additionally, the study highlights the potential biases in AI outputs and the risk of hallucinations—where AI generates plausible but incorrect translations.
Human translators are found to have an edge in handling complex, context-dependent tasks and maintaining high-quality translations. However, LLM's ability to quickly produce translations makes it a competitive alternative, especially for large-scale, general-purpose translation tasks. The study concludes with recommendations for translators to adapt by developing specialized skills, embracing continuous learning, and leveraging AI tools to enhance their work.

Keywords: Large Language Model, AI translation, translation quality, human translators, natural language processing

References

ป้อมเพชร ตาณังกร และสัญชัย สุลักษณานนท์. (2565). การศึกษาการแปลวิเศษณานุประโยคแบบโครงสร้างในภาษาฝรั่งเศสด้วยเครื่องมือแปลภาษา Google Translate. วารสารสมาคมครูภาษาฝรั่งเศสแห่งประเทศไทยในพระราชูปถัมภ์ฯ, 143(45), 27-49.

รัชดา ปลาบู่ทอง. (2551). การศึกษาการใช้คำลงท้ายภาษาไทยในวรรณกรรมแปลเรื่อง อาร์ทิมิส ฟาวล์, อาร์ทิมิส ฟาวล์ ตอนมหันตภัยในอาร์กติก และอาร์ทิมิส ฟาวล์ ตอนรหัสลับนิรันดร. (วิทยานิพนธ์มหาบัณฑิต, มหาวิทยาลัยมหิดล]. [DOI: https://doi.nrct.go.th/ListDoi/listDetail?Resolve_DOI=10.14457/MU.the.2008.210].

สมบัติ ศิริจันดา และ จักกเมธ พวงทอง. (2564). ลักษณะเฉพาะของภาษาไทยที่มีผลต่อการแปลเป็นภาษาอังกฤษ: กรณีศึกษาของการแปลทางวิชาการ. วารสารศิลปศาสตร์ราชมงคลสุวรรณภูมิ, 3(3), 370-383.

SCB 10X. (2567). ส่องไอเดียพัฒนา AI: ไปดูกันว่า Typhoon (Thai LLM จาก SCB 10X) สามารถนำไปพัฒนานวัตกรรมอะไรได้บ้าง. ค้นเมื่อ 2 พฤษภาคม 2567, จาก https://www.scb10x.com/blog/explore-ai-development-ideas-typhoon-1-5x-thai-llm-scb-10x.

Alphanome.ai. (2023). Understanding token limits in large language models: Use cases, techniques, and overcoming limitations. Retrieved 24 July 2024, from https://www.alphanome.ai/post/understanding-token-limits-in-large-language-models-usecases-techniques-and-overcoming-limitatios.

ASTM International. (2023). Standard guide for quality assurance in translation. Retrieved 2 May 2024, from https://www.astm.org/f2575-14.html.

Baker, M., & Saldanha, G. (Eds.). (2009). Routledge encyclopedia of translation studies. (2nd ed.). New York: Routledge.

Bowker, L. (2024, 16 July). BTi Public Talk: Research trends in translation technology. Retrieved 22 July 2024, from https://fb.watch/ttaMad607U/.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D M., Wu, J., Winter, C,... Amodei, D. (2020). Language models are few-shot learners. Retrieved 6 May 2024, from https://arxiv.org/pdf/2005.14165.

Castilho, S. (2024). BTi Public Talk: The translation profession in the age of AI: Realities and possibilities. Retrieved 6 May 2024, from https://fb.watch/tuArI4nVSZ/.

Deepchecks. (2023). Five approaches to solve LLM token limits. Retrieved 22 July 2024, from https://deepchecks.com/5-approaches-to-solve-llm-token-limits/.

Dokutech Translations. (n.d.). LLM vs NMT: An in-depth analysis of machine translation models. Retrieved 1 December 2024 from https://dokutechtranslations.com/en/llm-vs-nmt-anin-depth-analysis-of-machine-translation-models/.

Drugan, J. (2013). Quality in professional translation: Assessment and improvement. London: Bloomsbury.

Feng, Z., Zhang, Y., Li, H., Liu, W., Lang, J., Feng, Y., Wu, J., & Liu, Z. (2024). Improving LLM-based machine translation with systematic self-correction. Retrieved 2 May 2024, from https://arxiv.org/pdf/2402.16379v2.

Haan, K. (2023). Over 75% of consumers are concerned about misinformation from artificial intelligence. Retrieved 25 July 2024, from https://www.forbes.com/advisor/business/artificial-intelligence-consumer-sentiment/#over_75_are_concerned_about_artificial_intelligence_causing_job_loss_section.

Jiang, C. (2023). Investigation on the application of artificial intelligence large language model in translation tasks. In S. Yacob et al. (Eds.), Proceedings of the 2023 7th International Seminar on Education, Management and Social Sciences (ISEMSS 2023), (pp. 1342-1351).

Lee, T. K. (2023). Artificial intelligence and posthumanist translation: ChatGPT versus the translator. Retrieved 1 December 2024 from https://doi.org/10.1515/applirev-2023-0122.

Mahidol University International College. (n.d.). Large language models (LLM)-What are they?. Retrieved 2 July 2024, from https://muic.mahidol.ac.th/eng/large-language-modelsllm-what-are-they/.

Manyika, J., & Sneader, K. (2018). AI, automation, and the future of work: Ten things to solve for. Retrieved 2 July 2024, from https://www.mckinsey.com/featured-insights/future-ofwork/ai-automation-and-the-future-of-work-ten-things-tosolve-for/.

Mao, J. (2022). Differentiated measurements for fatigue and demotivation in translation process. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research), (pp. 1-14).

Marino, S. (2023, March 31). Translation rates 2023 – Complete (and honest) answer to why translation prices are so wildly different. Retrieved 12 December 2024, from https://abctranslations.com/translation-rates-2023-complete-andhonest-answer-to-why-translation-prices-are-so-wildly-different.

Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., & Clark, J. (2024). The AI index 2024 annual report (p. 479). AI Index Steering Committee, Institute for Human-Centered AI, Stanford University.

Moneus, A. M., & Sahari, Y. (2024). Artificial intelligence and human translation: A contrastive study based on legal texts. Retrieved 12 May 2024, from https://doi.org/10.1016/j.heliyon.2024.e28106.

NIST Multimodal Information Group. (2010). NIST 2005 Open Machine Translation (OpenMT) Evaluation LDC2010T14. Retrieved 2 May 2024, from https://catalog.ldc.upenn.edu/docs/LDC2010T14/DARPATIDESMT05EvalPlan_v1-1.pdf.

OpenAI. (2023). GPT-4 technical report. Retrieved 2 May 2024, from https:// openai.com/research/gpt-4.

OpenAI. (n.d.). Text generation. Retrieved 2 May 2024, from https://platform. openai.com/docs/guides/text-generation.

Pacific International Translations. (n.d.). Expected translation times by professional translators. Retrieved 2 May 2024, from https://www.pactranz.com/translation- times/.

ReiB, K., & Vermeer, H. J. (2014). Towards a general theory of translational action: Skopos theory explained (C. Nord, Trans.; M. Dudenhöfer, English reviewer). New York: Routledge.

Roberts, I. G., Watumull, J., & Chomsky, N. (2023). Xenolinguistics. New York: Routledge.

SCB 10X. (2024). Behind the working of large language model. Retrieved 2 May 2024, from https://www.scb10x.com/blog/large-language-model-explained.

Stefani, E. (2024). NMT vs. LLM: Which translation technology suits your needs?. Retrieved 2 November 2024, from https://www.languageline.com/blog/nmt-vs.-llm-which-translationtechnology-suits-your-needs.

Toolify. (2024). Mastering token limits in large language models. Retrieved 15 June 2024, from https://www.toolify.ai/ainews/mastering-token-limits-in-large-languagemodels-418929.

Wang, L. (2023). The impacts and challenges of artificial intelligence translation tool on translation professionals. Retrieved 12 December 2024, from https://doi.org/10.1051/shsconf/202316302021.

Zhang, X., Li, S., Hauer, B., Shi, N., & Kondrak, G. (2023). Don’t trust ChatGPT when your question is not in English: A study of multilingual abilities and types of LLMs. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, (pp. 7915-7927).

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Published

2024-12-31

How to Cite

Senapa, C., Sattayaporn, P., & Samrittitanon, L. (2024). Impact of Large Language Model on the Translation Profession: Opportunities and Challenges for Human Translators. Chophayom Journal, 35(3), 282–307. retrieved from https://so01.tci-thaijo.org/index.php/ejChophayom/article/view/275821