AraDetox: A Multi-Dialect Arabic Detoxification Dataset
2026-08-24 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors created AraDetox, a large dataset to help make harmful Arabic social media posts safer by rewriting them without changing their meaning. They used advanced AI models to generate these safer versions in different Arabic dialects and checked the results with human reviewers. Their study found that detoxifying text involves carefully changing words and sentence structure while keeping the original message intact. This work shows how combining AI and human checks can produce useful resources for making harmful text safer in Arabic.
Arabic detoxificationharmful language detectionGPT-5Gemini 2.5 Flashdialectal Arabicsemantic similaritylexical substitutionsocial media textlarge language modelstext rewriting
Authors
Mo El-Haj
Abstract
Arabic harmful-language detection has received considerable attention, yet Arabic text detoxification remains underexplored. We introduce AraDetox, a multi-dialect Arabic detoxification dataset comprising 10,500 harmful social-media posts and 84,000 detoxified rewrites generated using GPT-5 and Gemini 2.5 Flash across Modern Standard Arabic, Gulf, Levantine, and Egyptian Arabic. The generated outputs were assessed through human evaluation and automatic analyses of lexical change, semantic preservation, sentiment, and dialectal style. Results show that detoxification is primarily a meaning-preserving rewriting task: substantial lexical and structural reformulation is accompanied by consistently high semantic similarity. Human evaluation confirms successful harmful-language removal while largely preserving the original meaning. Dialectal analyses further indicate that the generated variants exhibit measurable stylistic alignment with reference Arabic dialect corpora. Comparison with existing resources highlights two complementary approaches to detoxification: minimal-edit lexical substitution and meaning-preserving reformulation. Our findings demonstrate that large-scale Arabic detoxification resources can be constructed through LLM-assisted generation and human verification. The dataset is publicly available at https://github.com/ArabicNLP-UK/AraDetox to support future research on Arabic detoxification, safe text generation, and multi-dialect Arabic NLP.