Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909336661393408 |
|---|---|
| author | Huang, Jen-tse Lam, Man Ho Li, Eric John Ren, Shujie Wang, Wenxuan Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. |
| author_facet | Huang, Jen-tse Lam, Man Ho Li, Eric John Ren, Shujie Wang, Wenxuan Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. |
| contents | Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes seven LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4, Mixtral-8x22B, and LLaMA-3.1. We find that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, i.e., EmotionBench, are publicly available at https://github.com/CUHK-ARISE/EmotionBench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03656 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench Huang, Jen-tse Lam, Man Ho Li, Eric John Ren, Shujie Wang, Wenxuan Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Computation and Language Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes seven LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4, Mixtral-8x22B, and LLaMA-3.1. We find that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, i.e., EmotionBench, are publicly available at https://github.com/CUHK-ARISE/EmotionBench. |
| title | Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.03656 |