让语言模型学出圆形情绪分布，揭示其优劣与适用场景
Are Emotions Arranged in a Circle? Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning
- 在超球面上用对比学习构建圆形情绪表示
- 圆形结构提升可解释性但高维下分类性能较差
- 适合需要直观情绪布局的场景，如人机交互
心理学长期使用环形模型组织情绪，相似情绪相邻、对立情绪相对。尽管常被用于解释深度学习表征，但该模型极少直接融入语言模型的表征学习中，其几何合理性未被验证。本文提出一种在超球面上通过对比学习诱导圆形情绪表示的方法。结果表明，这种环形对齐虽显著提升可解释性并增强降维鲁棒性，但在高维设置和细粒度分类任务上表现逊于传统设计。研究揭示了将心理环形模型应用于深度学习架构时的权衡关系。
原文摘要 · Abstract (English)
Psychological research has long utilized circumplex models to structure emotions, placing similar emotions adjacently and opposing ones diagonally. Although frequently used to interpret deep learning representations, these models are rarely directly incorporated into the representation learning of language models, leaving their geometric validity unexplored. This paper proposes a method to induce circular emotion representations within language model embeddings via contrastive learning on a hypersphere. We show that while this circular alignment offers superior interpretability and robustness against dimensionality reduction, it underperforms compared to conventional designs in high-dimensional settings and fine-grained classification. Our findings elucidate the trade-offs involved in applying psychological circumplex models to deep learning architectures.
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