Rethinking AI Cultural Evaluation

As AI systems become more integrated into society, evaluating their capacity to align with diverse cultural values is crucial for their responsible deployment. Current evaluation methods predominantly rely on multiple-choice question (MCQ) datasets. In this study, we demonstrate that MCQs are insufficient for capturing the complexity of cultural values expressed in open-ended scenarios. Our findings highlight significant discrepancies between MCQ-based assessments and the values conveyed in unconstrained interactions. Based on these findings, we recommend moving beyond MCQs to adopt more open-ended, context-specific assessments that better reflect how AI models engage with cultural values in realistic settings.

Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas

The success of Large Language Models (LLMs) in multicultural environments hinges on their ability to understand users' diverse cultural backgrounds. We measure this capability by having an LLM simulate human profiles representing various nationalities within the scope of a questionnaire-style psychological experiment. Specifically, we employ GPT-3.5 to reproduce reactions to persuasive news articles of 7,286 participants from 15 countries; comparing the results with a dataset of real participants sharing the same demographic traits. Our analysis shows that specifying a person's country of residence improves GPT-3.5's alignment with their responses. In contrast, using native language prompting introduces shifts that significantly reduce overall alignment, with some languages particularly impairing performance. These findings suggest that while direct nationality information enhances the model's cultural adaptability, native language cues do not reliably improve simulation fidelity and can detract from the model's effectiveness.