Contrast Set

Contrast sets are carefully constructed groups of minimally different data points designed to rigorously evaluate the robustness and reliability of machine learning models, particularly in complex domains like ethics, robotics, and natural language processing. Current research focuses on developing methods to generate effective contrast sets, often involving automated synonym substitution or targeted perturbations, and using them to assess model performance beyond simple accuracy metrics, such as by measuring consistency across the set. This approach helps identify biases, overfitting, and reliance on spurious correlations, leading to more reliable and generalizable models with improved performance and reduced societal biases in various applications.