语言模型表示中的情感压缩现象

AI 摘要

该研究在GoEmotions数据集上评估了Llama、Gemma和Qwen家族的七个指令微调模型，比较模型输出预测与情感提示下的末token表示。研究发现，在无监督KMeans聚类下，隐藏表示最清晰的划分是正负情感两类，较粗的情感分组也能用更少簇恢复，但线性探针仍保留大量细粒度情感预测信息，作者将这一现象称为相对GoEmotions分类体系的"过度压缩"。

正文节选

Compression of Emotion in Language Model Representations Abstract Large language models (LLMs) have been evaluated on their ability to identify emotions expressed in text and produce fluent emotional language. However, output predictions alone do not reveal how fine-grained emotion distinctions are organized internally. We study seven LLMs on GoEmotions, an English Reddit dataset annotated with 27 fine-grained emotion categories plus neutral. We compare model predictions and emotion-prompted fin

发布时间：2026-11-09 13:00

抓取时间：2026-11-09 13:15

来源机构：arXiv