Identification and Description of Emotions by Current Large Language Models

Abstract The assertion that artificial intelligence (AI) cannot grasp the complexities of human emotions has been a long-standing debate. However, recent advancements in large language models (LLMs) challenge this notion by demonstrating an increased capacity for understanding and generating human-like text. In this study, we evaluated the empathy levels and the identification and description of emotions by three current language models: Bard, GPT 3.5, and GPT 4. We used the Toronto Alexithymia Scale (TAS-20) and the 60-question Empathy Quotient (EQ-60) questions to prompt these models and score the responses. The models' performance was contrasted with human benchmarks of neurotypical controls and clinical populations. We found that the less sophisticated models (Bard and GPT 3.5) performed inferiorly on TAS-20, aligning close to alexithymia, a condition with significant difficulties in recognizing, expressing, and describing one's or others' experienced emotions. However, GPT 4 achieved performance close to the human level. These results demonstrated that LLMs are comparable in their ability to identify and describe emotions and may be able to surpass humans in their capacity for emotional intelligence. Our novel insights provide alignment research benchmarks and a methodology for aligning AI with human values, leading toward an empathetic AI that mitigates risk.