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Questionnaires for Everyone: Streamlining Cross-Cultural Questionnaire Adaptation with GPT-Based Translation Quality Evaluation

arXiv 2407.20608 ↗First posted 30 Jul 2024cs.HC cs.CLcited by 0

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Abstract (arXiv)

Adapting questionnaires to new languages is a resource-intensive process often requiring the hiring of multiple independent translators, which limits the ability of researchers to conduct cross-cultural research and effectively creates inequalities in research and society. This work presents a prototype tool that can expedite the questionnaire translation process. The tool incorporates forward-backward translation using DeepL alongside GPT-4-generated translation quality evaluations and improvement suggestions. We conducted two online studies in which participants translated questionnaires from English to either German (Study 1; n=10) or Portuguese (Study 2; n=20) using our prototype. To evaluate the quality of the translations created using the tool, evaluation scores between conventionally translated and tool-supported versions were compared. Our results indicate that integrating LLM-generated translation quality evaluations and suggestions for improvement can help users independently attain results similar to those provided by conventional, non-NLP-supported translation methods. This is the first step towards more equitable questionnaire-based research, powered by AI.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 34 references that are in the map. First posted on arXiv 30 Jul 2024.

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References · 3

  1. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 416
  2. 2023Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Modelscited by 87
  3. 2020Predicting Visual Importance Across Graphic Design Typescited by 30

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