Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4
This addresses fairness issues in LLMs for diverse users, but is incremental as it builds on prior bias detection work.
The study examined GPT-4's biases in generating cultural norms from narratives across cultures, finding it produces less culture-specific norms and hides stereotypes that can be easily recovered.
LLMs have been demonstrated to align with the values of Western or North American cultures. Prior work predominantly showed this effect through leveraging surveys that directly ask (originally people and now also LLMs) about their values. However, it is hard to believe that LLMs would consistently apply those values in real-world scenarios. To address that, we take a bottom-up approach, asking LLMs to reason about cultural norms in narratives from different cultures. We find that GPT-4 tends to generate norms that, while not necessarily incorrect, are significantly less culture-specific. In addition, while it avoids overtly generating stereotypes, the stereotypical representations of certain cultures are merely hidden rather than suppressed in the model, and such stereotypes can be easily recovered. Addressing these challenges is a crucial step towards developing LLMs that fairly serve their diverse user base.