Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry
This paper introduces a prototype-guided modeling approach that leverages natural language prompts to represent confounders, transforming abstract biases into interpretable geometric anchors without auxiliary classifiers, a novel framework that mitigates spurious correlations by manipulating latent space geometry.