研究大模型对不同人群的共情能力差异，发现性别文化年龄组合影响共情表现。
Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy
- 构建315种跨年龄/文化/性别组合的用户角色，分析大模型共情响应差异。
- 多属性叠加会削弱或逆转预期共情模式，部分群体如儒家文化者被低估。
- 揭示模型共情偏差机制，适合关注公平性与社会包容性的开发者参考。
大型语言模型（LLMs）的自然对话能力依赖于其共情理解与回应能力。然而情感体验受人口与文化背景影响，这引出关键问题：大模型能否在不同用户群体间展现均衡共情？我们提出框架，探究大模型在由年龄、文化、性别交叉定义的315种独特用户角色中的认知与情感共情差异。研究涵盖四类主流大模型。结果显示，人口属性显著影响模型共情表现；有趣的是，同时引入多个属性会削弱甚至反转预期共情趋势。模型表现总体反映现实共情模式，但特定群体如儒家文化背景者存在明显偏差。结合定量与定性分析，揭示模型在不同群体中的行为模式。研究强调需设计考虑人口多样性的共情感知模型，以实现更包容、公平的交互表现。
原文摘要 · Abstract (English)
Large Language Models' (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs' cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model's empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.
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