EmoBench: Evaluating the Emotional Intelligence of Large Language Models
Sahand Sabour Siyang Liu Zheyuan Zhang June M. Liu Jinfeng Zhou Alvionna S. Sunaryo Juanzi Li Tatia M. C. Lee Rada Mihalcea Minlie Huang
Recent advances in Large Language Models (LLMs) have highlighted the need for
robust, comprehensive, and challenging benchmarks. Yet, research on evaluating
their Emotional Intelligence (EI) is considerably limited. Existing benchmarks
have two major shortcomings: first, they mainly focus on emotion recognition,
neglecting essential EI capabilities such as emotion regulation and thought
facilitation through emotion understanding; second, they are primarily
constructed from existing datasets, which include frequent patterns, explicit
information, and annotation errors, leading to unreliable evaluation. We
propose EmoBench, a benchmark that draws upon established psychological
theories and proposes a comprehensive definition for machine EI, including
Emotional Understanding and Emotional Application. EmoBench includes a set of
400 hand-crafted questions in English and Chinese, which are meticulously
designed to require thorough reasoning and understanding. Our findings reveal a
considerable gap between the EI of existing LLMs and the average human,
highlighting a promising direction for future research. Our code and data will
be publicly available from https://github.com/Sahandfer/EmoBench.