大模型自发形成类人类情绪层级结构，且越大越复杂。
Emergence of Hierarchical Emotion Organization in Large Language Models
- 基于情感轮理论，分析模型输出的情绪依赖关系。
- 模型生成的情绪树与人类心理模型一致，规模越大越复杂。
- 发现社会身份交叉群体存在系统性情绪误判，适合伦理研究者关注。
随着大型语言模型（LLMs）日益用于对话代理，理解其如何建模用户情绪状态对伦理部署至关重要。受情感轮（emotion wheels）——一种认为情绪呈层级组织的心理学框架——启发，我们分析了模型输出中情绪状态间的概率依赖关系。结果发现，LLMs自然形成了与人类心理学模型一致的层级情绪树，且模型越大，其情绪层级越复杂。我们还发现，在不同社会经济人格画像中，情绪识别存在系统性偏差，对交叠的、代表性不足群体的误分类呈累积趋势。人类研究表明，这种模式与真实感知高度相似，暗示LLMs内化了部分社会认知机制。本研究不仅揭示了大模型中涌现的情绪推理能力，也提示可借助具认知基础的理论来改进模型评估体系。
原文摘要 · Abstract (English)
As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emotion wheels, i.e., a psychological framework that argues emotions organize hierarchically, we analyze probabilistic dependencies between emotional states in model outputs. We find that LLMs naturally form hierarchical emotion trees that align with human psychological models, and larger models develop more complex hierarchies. We also uncover systematic biases in emotion recognition across socioeconomic personas, with compounding misclassifications for intersectional, underrepresented groups. Human studies reveal striking parallels, suggesting that LLMs internalize aspects of social perception. Beyond highlighting emergent emotional reasoning in LLMs, our results hint at the potential of using cognitively-grounded theories for developing better model evaluations.
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