Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement
#hate speech detection #large language models #data augmentation #feature enhancement #machine learning
📌 Key Takeaways
- Researchers propose using large language models (LLMs) for hate speech detection.
- The approach incorporates data augmentation to improve model training.
- Feature enhancement techniques are applied to boost detection accuracy.
- The method aims to address limitations in existing hate speech detection systems.
📖 Full Retelling
arXiv:2603.04698v1 Announce Type: cross
Abstract: 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, P
🏷️ Themes
AI Ethics, Natural Language Processing
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Artificial intelligence
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Reinforcement learning
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Educational technology
2 shared
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Benchmark
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OpenAI
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Mentioned Entities
Original Source
--> Computer Science > Computation and Language arXiv:2603.04698 [Submitted on 5 Mar 2026] Title: Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement Authors: Brian Jing Hong Nge , Stefan Su , Thanh Thi Nguyen , Campbell Wilson , Alexandra Phelan , Naomi Pfitzner View a PDF of the paper titled Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement, by Brian Jing Hong Nge and 5 other authors View PDF HTML Abstract: 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 , weighted loss determined by inverse class proportions, Part-of-Speech 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 the Stormfront dataset. The study confirms that implicit hate speech is more difficult to detect than explicit hateful content and that enhancement effectiveness depends on dataset, model, and technique interaction. Our research informs the development of hate speech detection by highlighting how dataset properties, model architectures, and enhancement strategies interact, supporting more accurate and context-aware automated detection. Comments: Accepted for publication in the Proceedings of the 8th International Conference on Natural Language Processing (ICNLP 2026) Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04698 [cs.CL] (or arXiv:2603.04698v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04698 Focus to learn more arXiv-issued DO...
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