Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy

Abstract

Counterfactual data augmentation (CDA) is a promising strategy for improving hate speech classification, but automating counterfactual text generation remains a challenge. Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment. We propose **Gradient-assisted Energy-based Sampling (GENES)** for counterfactual text generation, which restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment. Compared to other methods that solely rely on either prompting, gradient-based steering, or energy-based sampling, GENES is more likely to jointly satisfy attribute alignment and semantic preservation under the same base model. When applied to data augmentation, GENES achieved the best macro F1-score in two of three test sets, and it improved robustness in detecting targeted abusive language. In some cases, GENES exceeded the performance of prompt-based methods using a GPT-4o-mini, despite relying on a smaller model (Flan-T5-Large). Based on our cross-dataset evaluation, the average performance of models aided by GENES is the best among those methods that rely on a smaller model (Flan-T5-L). These results position GENES as a possible lightweight and open-source alternative.