Large Language Models (LLMs) have shown remarkable abilities in understanding and generating human-like language, but how do they fare when it comes to emotional intelligence? Recent studies have evaluated LLMs' Emotional Intelligence (EI), focusing on emotion recognition, interpretation, and understanding.
Researchers assessed various mainstream LLMs using a novel psychometric assessment, and most achieved above-average EQ scores. Notably, GPT-4 exceeded 89% of human participants with an EQ score of 117. LLMs were tested on their ability to comprehend complex emotions in realistic scenarios. While they performed well, some models apparently didn't rely on human-like mechanisms to achieve human-level performance.
The study suggests that factors like model size, training method, and architecture influence LLMs' EQ. Further research is needed to understand these dynamics. A benchmark designed to evaluate LLMs' emotional intelligence, known as EQ-Bench, assesses their ability to understand complex emotions and social interactions.
Improving LLMs' emotional intelligence can lead to more natural and effective human-AI interactions, benefiting applications like customer service, sales, and healthcare. Emotionally intelligent LLMs can generate more contextually appropriate and human-like responses, increasing user engagement and trust. Continued research in this area may lead to more advanced AI systems that truly understand and respond to human emotions.
