Heartificial Intelligence：探索语言模型中的共情能力
Heartificial Intelligence: Exploring Empathy in Language Models
AI总结：
本研究通过标准化心理测试发现，大型语言模型在认知共情任务上优于人类，但情感共情显著低于人类，这使其能在避免情感疲劳和偏见的情况下提供客观一致的情感支持。
摘要：
大型语言模型变得越来越普遍，全球有数百万人将其应用于职业和个人情境中。随着这些模型不断进步，它们经常被用作虚拟助手和伴侣。在人类互动中，有效的沟通通常涉及两种共情：认知共情（理解他人的想法和情绪）和情感共情（在情感上分享他人的感受）。在本研究中，我们使用标准化的心理测试，考察了几种小型语言模型（SLMs）和大型语言模型（LLMs）的认知共情和情感共情。我们的结果显示，LLMs在认知共情任务上始终优于人类，包括心理学专业的学生。然而，尽管具有认知优势，小型和大型语言模型表现出的情感共情均显著低于人类参与者。这些发现突显了语言模型在模拟认知共情能力方面的快速进步，表明其在提供有效的虚拟陪伴和个性化情感支持方面具有巨大潜力。此外，它们较高的认知共情但较低的情感共情，使其能够提供客观且一致的情感支持，而不会面临情感疲劳或偏见的风险。
英文摘要：
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.
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。
