Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement
This paper evaluates data augmentation and feature enhancement techniques for hate speech detection, comparing traditional classifiers, e.g., Delta Term Frequency-Inverse Document Frequency (Delta TF-IDF), with transformer-based models (DistilBERT, RoBERTa, DeBERTa, Gemma-7B, gpt-oss-20b) across diverse datasets. It examines the impact of Synthetic Minority Over-sampling Technique (SMOTE), weighted loss determined by inverse class proportions, Part-of-Speech (POS) tagging, and text data augmentation on model performance. The open-source gpt-oss-20b consistently achieves the highest results. On the other hand, Delta TF-IDF responds strongly to data augmentation, reaching 98.2% accuracy on ...
| Authors | Brian Jing Hong Nge, Stefan Su, Thanh Thi Nguyen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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