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.

翻译：将问卷适应新语言是一个资源密集型过程，通常需要聘请多位独立翻译人员，这限制了研究人员开展跨文化研究的能力，并在研究和社会中实际造成了不平等。本研究提出了一种能够加速问卷翻译过程的原型工具。该工具整合了基于DeepL的正向-反向翻译，以及GPT-4生成的翻译质量评估与改进建议。我们进行了两项在线研究，参与者使用我们的原型工具将问卷从英语翻译为德语（研究1；n=10）或葡萄牙语（研究2；n=20）。为评估使用该工具生成的翻译质量，我们比较了传统翻译版本与工具辅助版本的评价分数。结果表明，整合LLM生成的翻译质量评估与改进建议，能够帮助用户独立获得与传统非NLP支持的翻译方法相近的结果。这是迈向由人工智能驱动的、更公平的问卷研究的第一步。