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

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arxiv 2407.20608 v1 pith:B2YFDAHG submitted 2024-07-30 cs.HC cs.CL

classification cs.HCcs.CL
keywords translationqualityquestionnairesresearchtoolcross-culturalevaluationevaluations
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring LLMs for Automated Generation and Adaptation of Questionnaires

    cs.HC 2025-01 conditional novelty 5.0 of 10

    LLM-generated survey questions were rated as clear and specific, while LLM-based pretesting improved some adapted questions but often made original questions wordier and less clear.

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