GPT-4从第三人称视角模拟平均人类情感认知
GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective
AI总结：
本文研究GPT-4的情感推理能力，发现其从第三人称视角模拟普通人类的情感认知，在判断他人情绪时比自我归因更准确，表明普通观察者立场对下游应用更具相关性。
摘要：
本文扩展了近期关于大型语言模型（LLMs）情感推理能力的研究。当前针对LLMs的研究尚未直接评估LLMs预测情绪自我归因与他人情绪感知之间的差异。我们首先考察了精心设计的情绪诱发刺激，这些刺激最初旨在寻找代表他人细粒度推断情感归因的大脑神经活动模式。我们表明GPT-4在对此类刺激进行推理时尤为准确。这表明，在刻板场景中，LLMs对他人情绪归因的认同度显著高于在特异情境中对情绪自我归因的认同度。为进一步探讨此问题，我们的第二项研究利用了一个包含作者和第三人称视角标注的数据集。我们发现，GPT-4的解释与人类对他人情绪的判断更为一致，而非与自我评估更一致。值得注意的是，传统的情感计算模型主要依赖自我报告的真实情况作为黄金标准。然而，LLMs似乎采取的普通观察者立场，在缺乏个体信息和充分安全考量时，可能对许多下游应用更具相关性。
英文摘要：
This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.
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。
