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.

翻译：近年来，大语言模型（LLMs）的进展凸显了对鲁棒、全面且具有挑战性基准的需求。然而，关于评估其情绪智能（EI）的研究仍相当有限。现有基准存在两大缺陷：首先，它们主要聚焦于情绪识别，忽略了情绪调节、通过情绪理解促进思维等关键EI能力；其次，它们主要基于现有数据集构建，这些数据集包含频繁模式、显式信息及标注错误，导致评估不可靠。我们提出EmoBench，该基准基于成熟心理学理论，为机器情绪智能提出包含情绪理解与情绪应用的综合定义。EmoBench包含400道精心设计的英文与中文人工策问题，要求进行深入推理与理解。研究结果表明，现有LLMs的情绪智能与普通人之间存在显著差距，为未来研究指明了方向。我们的代码与数据将公开于https://github.com/Sahandfer/EmoBench。