AraDetox: A Multi-Dialect Arabic Detoxification Dataset

2026-08-24

AI Summary

为解决阿拉伯语有害语言净化问题，通过GPT-5和Gemini 2.5 Flash生成了包含多种方言的10,500条有害社交媒体帖子及84,000条净化改写版本的数据集AraDetox。

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.

Problem

• Arabic detoxification
• harmful-language removal
• dialectal style
• semantic preservation

Innovation

• Arabic detoxification
• multi-dialect dataset
• LLM-assisted generation
• semantic preservation
• dialectal style alignment