Summary of Giebench: Towards Holistic Evaluation Of Group Identity-based Empathy For Large Language Models, by Leyan Wang et al.
GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
by Leyan Wang, Yonggang Jin, Tianhao Shen, Tianyu Zheng, Xinrun Du, Chenchen Zhang, Wenhao Huang, Jiaheng Liu, Shi Wang, Ge Zhang, Liuyu Xiang, Zhaofeng He
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces GIEBench, a novel benchmark for evaluating the empathy of large language models (LLMs) towards diverse group identities. The existing benchmarks primarily focus on universal human emotions, neglecting the context of individuals’ group identities. GIEBench consists of 11 identity dimensions, covering 97 group identities with 999 single-choice questions related to specific group identities such as gender, age, occupation, and race. This benchmark aims to assess LLMs’ ability to respond from the perspective of identified groups, supporting the development of empathetic LLM applications tailored to users with different identities. The evaluation of 23 LLMs reveals that they can understand different identity standpoints but fail to consistently exhibit equal empathy without explicit instructions. This highlights the need for improved alignment of LLMs with diverse values to accommodate human identities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well large language models (LLMs) understand and care about people from different groups, such as women, men, young people, old people, and people from different countries. Right now, most tests just focus on general feelings like sadness and happiness, but this benchmark looks at specific group identities like gender, age, occupation, and race. The test has 11 categories with many questions to see how well LLMs can understand and respond from the perspective of these groups. They found that some LLMs are good at understanding different perspectives, but they often don’t show equal care for all groups without special instructions. This shows that we need better training for LLMs so they can better understand and respect people’s differences. |
Keywords
» Artificial intelligence » Alignment
