metadata

configs:
  - config_name: emotional_understanding
    data_files: EU.json
  - config_name: emotional_application
    data_files: EA.json
license: mit
task_categories:
  - question-answering
language:
  - en
  - zh
tags:
  - EmotionalIntelligence
  - EI
  - Theory_of_mind
size_categories:
  - n<1K

EmoBench

This is the official repository for our paper "EmoBench: Evaluating the Emotional Intelligence of Large Language Models"

Overview

EmoBench is a comprehensive and challenging benchmark designed to evaluate the Emotional Intelligence (EI) of Large Language Models (LLMs). Unlike traditional datasets, EmoBench focuses not only on emotion recognition but also on advanced EI capabilities such as emotional reasoning and application.

The dataset includes 400 hand-crafted scenarios in English and Chinese, structured into two key evaluation tasks:

Emotional Understanding (EU): Recognizing emotions and their causes in complex scenarios.
Emotional Application (EA): Recommending effective emotional responses or actions in emotionally charged dilemmas.

Key Features

Psychology-based Design: Grounded in established theories of Emotional Intelligence (e.g., Salovey & Mayer, Goleman).
Bilingual Support: Scenarios are available in both English and Chinese.
Challenging Scenarios: Includes nuanced emotional dilemmas that require reasoning and perspective-taking.
Annotations: High-quality multi-label annotations verified through rigorous inter-annotator agreement (Fleiss' Kappa = 0.852).

Dataset Structure

Emotional Understanding

Categories: Complex Emotions, Emotional Cues, Personal Beliefs and Experiences, Perspective-taking.
Example: Scenario: After a long day of terrible events, Sam started laughing hysterically when his car broke down. Task: Identify the emotion (e.g., sadness, joy) and its cause.

Emotional Application

Categories: Divided based on Relationship types (Personal, Social), Problem types (Self, Others) and Question types (Response, Action).
Example: Scenario: Rebecca's son lost his soccer game and is feeling upset and blaming himself. Task: identify the most effective response or action.

Evaluation

For code regarding evaluation, please visit our repository on GitHub.

Citation

If you find our work useful for your research, please kindly cite our paper as follows:

@article{EmoBench2024,
     title={EmoBench: Evaluating the Emotional Intelligence of Large Language Models}, 
      author={Sahand Sabour and Siyang Liu and Zheyuan Zhang and June M. Liu and Jinfeng Zhou and Alvionna S. Sunaryo and Juanzi Li and Tatia M. C. Lee and Rada Mihalcea and Minlie Huang},
            year={2024},
      eprint={2402.12071},
      archivePrefix={arXiv},