A growing amount of literature critiques the current operationalizations of empathy based on loose definitions of the construct. Such definitions negatively affect dataset quality, model robustness, and evaluation reliability. We propose an empathy evaluation framework that operationalizes empathy close to its psychological origins. The framework measures the variance in responses of LLMs to prompts using existing metrics for empathy and emotional valence. The variance is introduced through the controlled generation of the prompts by varying social biases affecting context understanding, thus impacting empathetic understanding. The control over generation ensures high theoretical validity of the constructs in the prompt dataset. Also, it makes high-quality translation, especially into languages that currently have little-to-no way of evaluating empathy or bias, such as the Slavonic family, more manageable. Using chosen LLMs and various prompt types, we demonstrate the empathy evaluation with the framework, including multiple-choice answers and free generation. The variance in our initial evaluation sample is small and we were unable to measure convincing differences between the empathetic understanding in contexts given by different social groups. However, the results are promising because the models showed significant alterations their reasoning chains needed to capture the relatively subtle changes in the prompts. This provides the basis for future research into the construction of the evaluation sample and statistical methods for measuring the results.

本研究针对当前同情心定义松散的问题，提出了一种基于心理学起源的同情心评估框架，以提高数据集质量和模型评估的可靠性。研究发现，通过控制生成的提示中社会偏见的变化，能够有效测量大型语言模型对同情心的理解差异，为未来评估方法的构建奠定了基础。

交叉同情偏见和理解的定量评估