LLM对所有人都具有同理心吗？调查多人口统计角色对模型同理心的影响
Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy
AI总结：
本研究提出交叉分析框架，考察四个LLM在315个人口统计角色上的认知与情感同理心，发现属性组合可改变同理心模式，并强调设计考虑多样性的同理心感知模型以促进公平。
摘要：
大型语言模型（LLMs）自然对话的能力源于其能够以同理心理解和回应用户。然而，情感体验受到人口统计和文化背景的影响。这引发了一个重要问题：LLM能否在不同用户群体中表现出公平的同理心？我们提出了一个框架来调查LLM的认知和情感同理心如何随由交叉人口统计属性定义的用户角色而变化。我们的研究引入了一种新颖的交叉分析，涵盖由年龄、文化和性别组合构建的315个独特角色，跨越四个LLM。结果表明，属性深刻影响模型的同理心回应。有趣的是，我们发现同时添加多个属性可以减弱并逆转预期的同理心模式。我们表明，它们大致反映了现实世界中的同理心趋势，但对某些群体（如儒家文化群体）存在显著的不一致。我们通过定性见解补充定量发现，以揭示不同人口统计群体间的模型行为模式。我们的发现强调了设计考虑人口多样性的同理心感知LLM的重要性，以促进更包容和公平的模型行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
