Cultural Bias in Language Models
Overview
Cultural bias research asks whether language models represent and respond to different communities’ knowledge, values, identities, and social norms fairly, rather than defaulting to dominant or English-language perspectives. Researchers build locally authored multilingual benchmarks and compare model answers, generated stories, safety decisions, and survey-style value distributions with native-speaker judgments and population data. Mitigation approaches include culturally grounded training and retrieval, persona or prompt conditioning, preference calibration, and activation-level steering, alongside audits of data and post-training. A central tension is that models may know culturally relevant facts but fail to apply them, while stronger alignment can flatten within-culture diversity or impose stereotypes; reliable measurement across languages and contexts remains difficult.
Latest papers
129 papers9 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.