Article Dans Une Revue Data and Knowledge Engineering Année : 2025

“Detectors Lead, LLMs Follow”: Integrating LLMs and traditional models on implicit hate speech detection to generate faithful and plausible explanations

Résumé

Social media platforms face a growing challenge in addressing abusive content and hate speech, particularly as traditional natural language processing methods often struggle with detecting nuanced and implicit instances. To tackle this issue, our study enhances Large Language Models (LLMs) in the detection and explanation of implicit hate speech, outperforming classical approaches. We focus on two key objectives: (1) determining whether jointly predicting and generating explanations for why a message is hateful improves LLMs' accuracy, especially for implicit cases, and (2) evaluating whether incorporating information from BERT-based models can further boost detection and explanation performance. Our method evaluates and enhances LLMs' ability to detect hate speech and explain their predictions. By combining binary classification (Hate Speech vs. Non-Hate Speech) with natural language explanations, our approach provides clearer insights into why a message is considered hateful, advancing the accuracy and interpretability of hate speech detection.

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hal-05386904 , version 1 (08-12-2025)

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Greta Damo, Nicolás Benjamín Ocampo, Elena Cabrio, Serena Villata. “Detectors Lead, LLMs Follow”: Integrating LLMs and traditional models on implicit hate speech detection to generate faithful and plausible explanations. Data and Knowledge Engineering, 2025, 162, ⟨10.1016/j.datak.2025.102535⟩. ⟨hal-05386904⟩
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