Extending the Contact Hypothesis: Cross-Linguistic Evaluation of Religion and Nationality Bias When Prompting LLMs in German and Icelandic

Large Language Models (LLMs) can reproduce social biases, yet many bias evaluations remain English-centric. We extend the Contact Hypothesis framework presented in previous work to German and Icelandic, focusing on religion and nationality. Evaluating GPT models (3.5, 4, 4-turbo, 4o, 5), we find that positive contact reduces biases in the answers of the LLMs, while negative contact amplifies it, with cross-linguistic differences in magnitude and salience.Our results support the cross-linguistic robustness of contact-based probing and underscore the need for culturally contextualized evaluations. In addition to these insights, our contributions lies in the dataset that is made available on Github for further research.