Occupational Prompting Reveals Cultural Bias in Large Language Models
A new research paper explores how occupational identities influence the responses of large language models (LLMs) to value-based questions. By using occupational prompts such as 'accountant' or 'nurse' instead of nationality-based prompts, researchers found that LLMs still exhibit a Western-leaning cultural bias, but specific occupations cause shifts within this regional bias. This suggests that LLMs interpret occupational cues as eliciting structured value patterns rather than neutral role labels, offering a new method to study cultural bias in AI. AI
IMPACT This research provides a novel method for evaluating and understanding cultural biases embedded within LLMs, potentially leading to more equitable AI systems.