Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs' overall emotional intelligence. Therefore, this paper presents a novel framework named EmotionQueen for evaluating the emotional intelligence of LLMs. The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition. LLMs are requested to recognize important event or implicit emotions and generate empathetic response. We also design two metrics to evaluate LLMs' capabilities in recognition and response for emotion-related statements. Experiments yield significant conclusions about LLMs' capabilities and limitations in emotion intelligence.

本研究针对现有情感分析研究不足以全面评估大型语言模型（LLM）情感智能的问题，提出了一个名为“情感女王”的新框架。该框架通过四个独特任务评估LLM的情感智能，并设计了两项评估指标来衡量其在情感识别和回应能力上的表现。实验结果显著揭示了LLM在情感智能方面的能力和局限性。

情感女王：评估大型语言模型同理心的基准