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BukuTHE USE OF LARGE LANGUAGE MODELS IN MEDICAL TRANSLATION: INSIGHTS FROM TRANSLATORS ON KNOWLEDGE, WILLINGNESS, AND CONCERNS
Bibliografi
Author: Charming, Gabriel Griffin ; Yani, Ani (Advisor)
Topik: Key words: Large Language Model; Complex medical texts; Translators’ perception; Translation research aid.
Bahasa: (EN )    
Penerbit: Department of English Language Education Faculty of Education and Language Atma Jaya Catholic University of Indonesia     Tempat Terbit: Jakarta    Tahun Terbit: 2025    
Jenis: Theses - Undergraduate Thesis - Abstract of Undergraduate Thesis
Fulltext:
Abstract
With the turn of the 21st Century, AI technologies such as LLMs have revolutionized many fields, including medical translation. However, recently, doubts regarding the accuracy of the information provided by these LLMs have formed. Thus, a necessity to gauge how translators perceived the information given by LLMs was born. This research aimed to gauge the translators' perceptions towards the use of Large Language Models in personal research to raise translation quality in medical texts. This research utilized a mixed method with the quantitative phase utilizing an 18-item online survey involving 3 male and 4 female members of the production team at a translation agency in Yogyakarta, while the qualitative phase utilized 7 semi structured interview questions with 3 male and 2 female production team members. The findings were analyzed based on three aspects: The members’ knowledge of Large Language Model technologies, their willingness to use Large Language Model Technologies, as well as concerns regarding Large Language Model Technologies. The results showed that members of the production team were knowledgeable and familiar with Large Language Model technologies and are willing to use them. However, they also believed that Large Language Models should not be the main source of information and should be supported by additional research, especially when it comes to understanding complex terms in medical texts during the translation process. In addition, the production team members believed that the concerns regarding Large Language Model technologies are not really justified and that using this technology, much like other technologies, requires caution.
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