Towards Efficient and Explainable Hate Speech Detection via Model Distillation

Understand

Automatic detection of hate and abusive language is essential to combat its online spread.

• Moreover, recognising and explaining hate speech serves to educate people about its negative effects.
• However, most current detection models operate as black boxes, lacking interpretability and explainability.
• In this context, Large Language Models (LLMs) have proven effective for hate speech detection and to promote interpretability.