Abstract

In an era where artificial intelligence is increasingly interfacing with diverse cultural contexts, the ability of language models to accurately represent and adapt to these contexts is of paramount importance. The present research undertakes a meticulous evaluation of three prominent commercial language models-Google Gemini 1.5, ChatGPT-4, and Anthropic's Claude 3 Sonet-with a focus on their handling of the Turkish language. Through a dual approach of quantitative metrics, the Cultural Inaccuracy Score (CIS) and the Cultural Sensitivity Index (CSI), alongside qualitative analyses via detailed case studies, disparities in model performances were highlighted. Notably, Claude 3 Sonet exhibited superior cultural sensitivity, underscoring the effectiveness of its advanced training methodologies. Further analysis revealed that all models demonstrated varying degrees of cultural competence, suggesting significant room for improvement. The findings emphasize the necessity for enriched and diversified training datasets, alongside innovative algorithmic enhancements, to reduce cultural inaccuracies and enhance the models' global applicability. Strategies for mitigating cultural hallucinations are discussed, focusing on the refinement of training processes and continuous model evaluation to foster improvements in AI cultural adaptiveness. The study aims to contribute to the ongoing refinement of AI technologies, ensuring they respect and accurately reflect the rich tapestry of human cultures.