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New dataset uses GPT-5 and Gemini to detoxify Arabic social media text

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced AraDetox, a new dataset designed to improve the detoxification of harmful Arabic language on social media. The dataset contains over 10,500 harmful posts and 84,000 rewritten, detoxified versions generated using GPT-5 and Gemini 2.5 Flash across various Arabic dialects. Human evaluations confirmed that the detoxification process effectively removes harmful content while preserving the original meaning and dialectal style, demonstrating the potential of LLM-assisted generation for creating large-scale Arabic NLP resources. AI

IMPACT This dataset could advance research in Arabic NLP and the development of safer online communication tools.

RANK_REASON The cluster contains an academic paper detailing a new dataset and methodology for Arabic text detoxification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset uses GPT-5 and Gemini to detoxify Arabic social media text

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The cluster contains an academic paper detailing a new dataset and methodology for Arabic text detoxification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 Română(RO) · Mo El-Haj ·

    AraDetox: A Multi-Dialect Arabic Detoxification Dataset

    arXiv:2608.22894v1 Announce Type: cross 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-med…

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