Exploring the Potential Role of Generative AI in the TRAPD Procedure for Survey Translation (2411.14472v1)

Abstract: This paper explores and assesses in what ways generative AI can assist in translating survey instruments. Writing effective survey questions is a challenging and complex task, made even more difficult for surveys that will be translated and deployed in multiple linguistic and cultural settings. Translation errors can be detrimental, with known errors rendering data unusable for its intended purpose and undetected errors leading to incorrect conclusions. A growing number of institutions face this problem as surveys deployed by private and academic organizations globalize, and the success of their current efforts depends heavily on researchers' and translators' expertise and the amount of time each party has to contribute to the task. Thus, multilinguistic and multicultural surveys produced by teams with limited expertise, budgets, or time are at significant risk for translation-based errors in their data. We implement a zero-shot prompt experiment using ChatGPT to explore generative AI's ability to identify features of questions that might be difficult to translate to a linguistic audience other than the source language. We find that ChatGPT can provide meaningful feedback on translation issues, including common source survey language, inconsistent conceptualization, sensitivity and formality issues, and nonexistent concepts. In addition, we provide detailed information on the practicality of the approach, including accessing the necessary software, associated costs, and computational run times. Lastly, based on our findings, we propose avenues for future research that integrate AI into survey translation practices.

The Role of Generative AI in Enhancing Survey Translation: An Analysis of its Potential and Challenges

This paper investigates the potential application of generative AI, specifically ChatGPT, in the preparation and translation of survey questions. The paper underscores the intricacies of translating survey instruments across various linguistic and cultural spheres, emphasizing the necessity for accuracy to maintain the validity and reliability of data collected. The challenge addressed is how generative AI can supplement existing translation methods, such as the TRAPD (Translation, Review, Adjudication, Pre-Test, and Documentation) procedure, to enhance the quality and coherence of survey translations.

Core Findings

The research centers around a computational experiment deploying ChatGPT to evaluate its ability to flag linguistic and conceptual issues in survey questions without prior customization or fine-tuning. The experiment processed 282 survey questions selected from various respected organizations, subjected to treatments involving different GPT models (GPT-3.5 and GPT-4) and target linguistic audiences (Spanish in Spain and Mandarin in China). The output was qualitatively analyzed for translation-related issues categorized into codes like inconsistent conceptualization, cultural terms, and sensitivity.

Significant findings include:

• Model Influence: The newer GPT-4 model did not uniformly surpass GPT-3.5, demonstrating specific strengths, such as identifying syntax issues and sensitivity concerns, while underperforming in recognizing technical and cultural specifics.
• Impact of Linguistic Context: Specifying the target linguistic audience affected the incidence of flagged issues, with different types of problems being highlighted depending on the specified context (e.g., Spanish vs. Chinese). This finding underscores the importance of contextual awareness in generative AI applications.
• Limitations and Interaction Effects: Interaction effects between model versions and linguistic audience specifications were noted, revealing nuanced performance variability and emphasizing the need for tailored prompts and configurations.

Practical Implications

Generative AI shows promise as a supplementary tool in survey translations, capable of preemptively identifying issues that may otherwise go unnoticed, reducing potential survey errors and saving costs associated with flawed data collection. The ability to flag culturally or linguistically challenging aspects could significantly aid researchers, especially in resource-constrained environments.

The process demonstrated logistical feasibility when employing AI, highlighting the balance between the cost of premium AI services and the demand for manual oversight. This suggests that, while beneficial, AI's integration must be carefully managed and should not entirely replace human expertise in survey translation practices.

Future Directions

The paper calls for further examination of AI's accuracy in translations and the optimal integration into established practices like the TRAPD method. Future research should assess AI's utility in less digitally prevalent languages and explore more sophisticated prompt engineering methods to enhance AI output. Additionally, there is a need to determine AI's role—either as an initial tool for translation preparation or as a secondary check post-human translation.

Conclusion

This paper contributes to the discourse on AI's application in social science research methodologies, positing that generative AI, while not a panacea, offers significant enhancements to traditional practices under specific conditions. The insights gained pave the way for broader integration of AI in survey research, reinforcing the role of these technologies in modern data collection methodologies. As AI capabilities continue to advance, so too will its potential to refine and enrich cross-cultural research endeavors.