SahandSab/EmoBench

Tasks

• question-answering

Citation text from the card

@inproceedings{sabour-etal-2024-emobench,
    title = "{E}mo{B}ench: Evaluating the Emotional Intelligence of Large Language Models",
    author = "Sabour, Sahand  and
      Liu, Siyang  and
      Zhang, Zheyuan  and
      Liu, June  and
      Zhou, Jinfeng  and
      Sunaryo, Alvionna  and
      Lee, Tatia  and
      Mihalcea, Rada  and
      Huang, Minlie",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.326",
    doi = "10.18653/v1/2024.acl-long.326",
    pages = "5986--6004",
    abstract = "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 management 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 

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