GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
This addresses the need for better alignment of LLMs with diverse human identities for empathetic AI applications, though it is incremental as it builds on existing empathy evaluation benchmarks.
The authors tackled the problem of evaluating empathy in large language models (LLMs) towards diverse group identities by introducing GIEBench, a benchmark with 999 questions across 97 group identities, and found that 23 tested LLMs fail to consistently show equal empathy without explicit instructions.
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical. Most existing benchmarks for empathy evaluation of LLMs focus primarily on universal human emotions, such as sadness and pain, often overlooking the context of individuals' group identities. To address this gap, we introduce GIEBench, a comprehensive benchmark that includes 11 identity dimensions, covering 97 group identities with a total of 999 single-choice questions related to specific group identities. GIEBench is designed to evaluate the empathy of LLMs when presented with specific group identities such as gender, age, occupation, and race, emphasizing their ability to respond from the standpoint of the identified group. This supports the ongoing development of empathetic LLM applications tailored to users with different identities. Our evaluation of 23 LLMs revealed that while these LLMs understand different identity standpoints, they fail to consistently exhibit equal empathy across these identities without explicit instructions to adopt those perspectives. This highlights the need for improved alignment of LLMs with diverse values to better accommodate the multifaceted nature of human identities. Our datasets are available at https://github.com/GIEBench/GIEBench.