ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models
<|reference_start|>ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models: Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-being. With the advancement of Large Language Models (LLMs), many researchers have employed LLMs as the ESC models. However, the evaluation of these LLM-based ESCs remains uncertain. Inspired by the awesome development of role-playing agents, we propose an ESC Evaluation framework (ESC-Eval), which uses a role-playing agent to interact with ESC models, followed by a manual evaluation of the interactive dialogues. In detail, we first re-organize 2,801 role-playing cards from seven existing datasets to define the roles of the role-playing agent. Second, we train a specific role-playing model called ESC-Role which behaves more like a confused person than GPT-4. Third, through ESC-Role and organized role cards, we systematically conduct experiments using 14 LLMs as the ESC models, including general AI-assistant LLMs (ChatGPT) and ESC-oriented LLMs (ExTES-Llama). We conduct comprehensive human annotations on interactive multi-turn dialogues of different ESC models. The results show that ESC-oriented LLMs exhibit superior ESC abilities compared to general AI-assistant LLMs, but there is still a gap behind human performance. Moreover, to automate the scoring process for future ESC models, we developed ESC-RANK, which trained on the annotated data, achieving a scoring performance surpassing 35 points of GPT-4. Our data and code are available at https://github.com/haidequanbu/ESC-Eval.<|reference_end|>
@article{zhao2024esc-eval:,
    title={ESC-Eval: Evaluating Emotion Support Conversations in Large Language
  Models},
    author={Haiquan Zhao and Lingyu Li and Shisong Chen and Shuqi Kong and Jiaan
  Wang and Kexin Huang and Tianle Gu and Yixu Wang and Wang Jian and Dandan
  Liang and Zhixu Li and Yan Teng and Yanghua Xiao and Yingchun Wang},
    journal={arXiv preprint arXiv:2406.14952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14952},
    primaryClass={cs.CL}
}