Disentangling Emotion Understanding and Generation in Large Language Models

AbstractLarge language models (LLMs) have demonstrated strong performance on emotion understanding tasks, yet their ability to faithfully generate emotionally aligned text remains less well understood.We propose a semantic evaluation framework that jointly assesses emotion understanding, emotion generation, and internal consistency, using a VAE-based emotion cost matrix that captures graded semantic similarity between emotion categories.Our framework introduces four complementary metrics that disentangle baseline understanding, human-perceived emotion in generated text, generation quality, and model consistency.Experimental results show that while understanding and consistency scores are highly correlated, emotion generation exhibits substantially weaker correlations with these metrics.These findings motivate the development of specialized evaluation protocols that independently measure emotional understanding and generation, enabling more reliable assessments of LLM emotional intelligence.