大模型能理解情绪但无法共情，适合做冷静助手。
Heartificial Intelligence: Exploring Empathy in Language Models
- 用心理测试评估大模型的认知与情感共情能力
- 大模型在认知共情上胜过人类，但情感共情远低于人
- 适合需要稳定支持的场景，如心理咨询辅助
大型语言模型已广泛应用于专业与个人场景，常作为虚拟助手和陪伴者。在人际沟通中，有效交流依赖认知共情（理解他人想法与情绪）与情感共情（情感上共享他人感受）。本研究通过标准化心理测试，评估了多个小型（SLMs）与大型语言模型（LLMs）在认知与情感共情方面的能力。结果表明，LLMs在认知共情任务中表现优于人类，包括心理学专业学生；然而，无论大小模型，其情感共情水平均显著低于人类参与者。这一发现表明，语言模型在模拟认知共情方面进步迅速，具备提供有效虚拟陪伴与个性化情感支持的潜力。同时，其高认知、低情感共情特性可实现客观一致的情感支持，避免情绪耗竭或偏见风险。
原文摘要 · Abstract (English)
Large language models have become increasingly common, used by millions of people worldwide in both professional and personal contexts. As these models continue to advance, they are frequently serving as virtual assistants and companions. In human interactions, effective communication typically involves two types of empathy: cognitive empathy (understanding others' thoughts and emotions) and affective empathy (emotionally sharing others' feelings). In this study, we investigated both cognitive and affective empathy across several small (SLMs) and large (LLMs) language models using standardized psychological tests. Our results revealed that LLMs consistently outperformed humans - including psychology students - on cognitive empathy tasks. However, despite their cognitive strengths, both small and large language models showed significantly lower affective empathy compared to human participants. These findings highlight rapid advancements in language models' ability to simulate cognitive empathy, suggesting strong potential for providing effective virtual companionship and personalized emotional support. Additionally, their high cognitive yet lower affective empathy allows objective and consistent emotional support without running the risk of emotional fatigue or bias.
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