Work

Leveraging Large Language Models for Enhanced Back-Translation: Techniques and Applications

Year: 2025

Type: article

Abstract: Cross-cultural studies are prevalent in academia, yet challenges arise in conducting objective research due to linguistic and cultural disparities. Rigorous international comparative research requires appropriate questionnaires that can be used in all countries, and translation becomes an extremely important process. Brislin’s back-translation method is widely recognized, but it usually requires many skilled bilingual translators and is both time-consuming and expensive. This study aims to overcome these limitations by using Large Language Model (LLM) AI technology. We utilized the Application Programming Interfaces (APIs) of well-known commercial LLM models such as ChatGPT3.5, ChatGPT4o, Google-Gemini, and Anthropic-Claude 3. The entire program was built using the Python programming language, and the user interface was built using the Streamlit library. This pilot study’s results confirm the feasibility of LLM-assisted back-translation, particularly for complex topics like carbon footprint reduction planning. This represents a significant advance over traditional back-translation methods, offering substantial time and cost savings.

Source: IEEE Access

DOI: 10.1109/access.2025.3557014

Authors: Ji-Bum Chung, Taehyun Kim

Institutions: Ulsan National Institute of Science and Technology, Korea Environment Institute

Language: English

Topic: Translation Studies and Practices

Subfield: Language and Linguistics

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Korea Environment Institute, National Research Foundation of Korea, Korea Agency for Infrastructure Technology Advancement