提出双视角框架，让大模型更懂不同文化中的情绪表达与理解差异。
Dual Perspectives in Emotion Attribution: A Generator-Interpreter Framework for Cross-Cultural Analysis of Emotion in LLMs
- 构建生成者-解释者双视角框架，兼顾情绪表达与解读的文化背景。
- 在15国数据上测试6个大模型，发现表现差异与情绪类型和文化相关。
- 强调模型生成者国籍影响更大，呼吁更注重文化敏感性的情绪建模。
大语言模型（LLMs）在跨文化情绪理解与适应系统中日益重要，而情绪受文化表达与解读规范的影响。然而，现有情绪归属研究多聚焦于解读，忽视了情绪生成者的文化背景。这种普遍性假设忽略了各国在情绪表达与感知上的差异。为此，我们提出生成者-解释者框架，从表达与解读双重视角捕捉情绪归属的跨文化特征。通过在15个国家的数据上对6个主流大模型进行系统评估，我们发现性能差异与情绪类型及文化语境密切相关。生成者与解释者之间的对齐效应显著，生成者所属国家对模型表现影响更强。研究呼吁在基于大模型的系统中引入文化敏感的情绪建模，以提升跨文化情绪理解的鲁棒性与公平性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used in cross-cultural systems to understand and adapt to human emotions, which are shaped by cultural norms of expression and interpretation. However, prior work on emotion attribution has focused mainly on interpretation, overlooking the cultural background of emotion generators. This assumption of universality neglects variation in how emotions are expressed and perceived across nations. To address this gap, we propose a Generator-Interpreter framework that captures dual perspectives of emotion attribution by considering both expression and interpretation. We systematically evaluate six LLMs on an emotion attribution task using data from 15 countries. Our analysis reveals that performance variations depend on the emotion type and cultural context. Generator-interpreter alignment effects are present; the generator's country of origin has a stronger impact on performance. We call for culturally sensitive emotion modeling in LLM-based systems to improve robustness and fairness in emotion understanding across diverse cultural contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。