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Evaluating Large Language Models' (LLMs) anthropomorphic capabilities hasbecome increasingly important in contemporary discourse. Utilizing the emotionappraisal theory from psychology, we propose to evaluate the empathy ability ofLLMs, i.e., how their feelings change when presented with specific situations.After a careful and comprehensive survey, we collect a dataset containing over400 situations that have proven effective in eliciting the eight emotionscentral to our study. Categorizing the situations into 36 factors, we conduct ahuman evaluation involving more than 1,200 subjects worldwide. With the humanevaluation results as references, our evaluation includes five LLMs, coveringboth commercial and open-source models, including variations in model sizes,featuring the latest iterations, such as GPT-4 and LLaMA-2. We find that,despite several misalignments, LLMs can generally respond appropriately tocertain situations. Nevertheless, they fall short in alignment with theemotional behaviors of human beings and cannot establish connections betweensimilar situations. Our collected dataset of situations, the human evaluationresults, and the code of our testing framework, dubbed EmotionBench, is madeopenly accessible via https://github.com/CUHK-ARISE/EmotionBench. We aspire tocontribute to the advancement of LLMs regarding better alignment with theemotional behaviors of human beings, thereby enhancing their utility andapplicability as intelligent assistants.

Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench