Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution

arXiv:2403.03121v1 Announce Type: new
Abstract: Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five …

abstract analysis applications arxiv attribution biases cs.cl emotion gap gender language language models large language large language models llms men nlp stereotypes type women