Quantitative Assessment of Intersectional Empathetic Bias and Understanding

TL;DR

This paper introduces a new framework for evaluating empathy in language models by measuring response variance to socially biased prompts, aiming for more valid and cross-lingual assessments.

Contribution

It operationalizes empathy measurement aligned with psychological theories and controls prompt generation to improve validity and translation quality.

Findings

Models showed significant changes in reasoning with different prompts.

Small variance observed in initial tests, indicating subtle sensitivity.

Framework provides a basis for future empathy evaluation research.

Abstract

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.