EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models

TL;DR

EmotionQueen introduces a comprehensive benchmark with four tasks and two metrics to evaluate large language models' emotional intelligence, focusing on recognition and empathetic response generation.

Contribution

The paper presents a novel framework, EmotionQueen, for assessing LLMs' emotional intelligence beyond basic sentiment analysis, including recognition and response tasks.

Findings

LLMs show strengths in explicit emotion recognition

Limitations in implicit emotional understanding

Challenges in generating empathetic responses

Abstract

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.