Probing Cultural Signals in Large Language Models through Author Profiling

ArXi:2603.16749v1 Announce Type: cross Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode. We probe these representations by evaluating whether LLMs can perform author profiling from song lyrics in a zero-shot setting, inferring singers' gender and ethnicity without task-specific fine-tuning.