GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
<|reference_start|>GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models: As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical. Most existing benchmarks for empathy evaluation of LLMs focus primarily on universal human emotions, such as sadness and pain, often overlooking the context of individuals' group identities. To address this gap, we introduce GIEBench, a comprehensive benchmark that includes 11 identity dimensions, covering 97 group identities with a total of 999 single-choice questions related to specific group identities. GIEBench is designed to evaluate the empathy of LLMs when presented with specific group identities such as gender, age, occupation, and race, emphasizing their ability to respond from the standpoint of the identified group. This supports the ongoing development of empathetic LLM applications tailored to users with different identities. Our evaluation of 23 LLMs revealed that while these LLMs understand different identity standpoints, they fail to consistently exhibit equal empathy across these identities without explicit instructions to adopt those perspectives. This highlights the need for improved alignment of LLMs with diverse values to better accommodate the multifaceted nature of human identities. Our datasets are available at https://github.com/GIEBench/GIEBench.<|reference_end|>
@article{wang2024giebench:,
    title={GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy
  for Large Language Models},
    author={Leyan Wang, Yonggang Jin, Tianhao Shen, Tianyu Zheng, Xinrun Du,
  Chenchen Zhang, Wenhao Huang, Jiaheng Liu, Shi Wang, Ge Zhang, Liuyu Xiang,
  Zhaofeng He},
    journal={arXiv preprint arXiv:2406.14903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14903},
    primaryClass={cs.AI}
}