揭示大模型对中国的地域偏见如何从刻板印象演变为具体社会决策。
Evaluating Regional Bias in LLMs From Abstract Stereotype to Concrete Social Decision-Making
- 构建从刻板印象到具体决策的评估框架S2D，覆盖中国34个省级行政区。
- 模型在能力与职业决策上表现出显著地域差异，且结果与经济数字发展指标相关。
- 偏见模式跨中英文提示稳定存在，适合关注AI公平性的研究者阅读。
大型语言模型（LLMs）中的地域偏见可能影响对区域群体的认知及对个体的社会决策。然而现有研究常将这些表现分开考察，难以揭示其内在结构与后果。本文提出Stereotypes-to-Decisions（S2D）框架，系统评估从抽象刻板印象到具体社会决策的地域偏见。覆盖中国全部34个省级行政区域，采用温暖度（感知友好性与可信度）和能力（感知能力和智力）的刻板印象评分，以及教育、职业、社交互动三类配对选择任务，评估六种主流模型。结果显示各地区得分存在显著差异，模型间一致性较高，尤其在能力和职业决策上。这些模式与区域经济及数字发展指标相关联，呈现混合的人类式刻板印象特征——某些地区在某一维度得分高而在另一维度低。偏见模式在中英文提示下保持基本稳定。总体表明，大模型中的地域偏见普遍存在、系统性强且后果显著，亟需更注重区域差异的评估与缓解策略。
原文摘要 · Abstract (English)
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, Occupation, and Social Interaction. Results reveal substantial regional differences in regional scores, with considerable agreement across models, especially for Competence and Occupation decisions. Furthermore, these patterns are associated with regional economic and digital development indicators and display mixed human-like stereotypes, with some regions rated highly on one dimension but poorly on the other. They also remain largely stable across Chinese and English prompts. Overall, our findings show that regional bias in LLMs is prevalent, systematic, and consequential, motivating more regionally aware evaluation and mitigation.
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