Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks

TL;DR

This paper introduces ClaD, a novel training paradigm that efficiently detects nuanced language like sexism, metaphors, and sarcasm by distilling target classes using Mahalanobis distance, achieving high performance with smaller models.

Contribution

ClaD is a new training method that improves pragmatic language understanding by combining Mahalanobis-based loss and interpretable decision algorithms, reducing computational costs.

Findings

ClaD outperforms baselines on sexism, metaphor, and sarcasm detection tasks.

Smaller models with ClaD match large language models' performance.

ClaD offers an efficient approach for classifying small target classes in diverse backgrounds.

Abstract

Detecting deviant language such as sexism, or nuanced language such as metaphors or sarcasm, is crucial for enhancing the safety, clarity, and interpretation of online social discourse. While existing classifiers deliver strong results on these tasks, they often come with significant computational cost and high data demands. In this work, we propose \textbf{Cla}ss \textbf{D}istillation (ClaD), a novel training paradigm that targets the core challenge: distilling a small, well-defined target class from a highly diverse and heterogeneous background. ClaD integrates two key innovations: (i) a loss function informed by the structural properties of class distributions, based on Mahalanobis distance, and (ii) an interpretable decision algorithm optimized for class separation. Across three benchmark detection tasks -- sexism, metaphor, and sarcasm -- ClaD outperforms competitive baselines, and even with smaller language models and orders of magnitude fewer parameters, achieves performance comparable to several large language models (LLMs). These results demonstrate ClaD as an efficient tool for pragmatic language understanding tasks that require gleaning a small target class from a larger heterogeneous background.