AI RESEARCH
Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Audit
arXiv CS.CL
•
ArXi:2605.30804v1 Announce Type: new We audit six large language models (LLMs) for gender stereotyping across English, Korean, Chinese, and Japanese. Three were developed primarily for English-language use (Claude, GPT, Gemini) and three for East Asian use (DeepSeek, Syn-Pro, HyperCLOVA X). We adopt the HEXACO-100 personality inventory and anchor each model against a cross-cultural human dataset spanning 48 countries to ask not whether LLMs are biased, but how far their gender attributions drift from the populations they are deployed among.