Evaluating Regional Bias in LLMs From Abstract Stereotype to Concrete Social Decision-Making
Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet existing studies often examine these manifestations separately, leaving their structure and consequences unclear. We introduce Stereotypes-to-Decisions (S2D), a systematic framework evaluating regional bias from abstract stereotypes to concrete social decisions. Covering all 34 provincial-level administrative regions of China, S2D evaluates six LLMs using stereotype ratings of Warmth (perceived friendliness and trustworthiness) and Competence (perceived capability and intelligence), along with paired-choice tasks across Education, Occupati...
| Authors | Jiayuan Di, Haoyi Yang, Yufei Luo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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