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arXiv AI By Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Hamdy Mubarak, George Mikros, Abul Hasnat, Wajdi Zaghouani

ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts

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ArGuard is a shared task that evaluates harmful content detection in Arabic memes and LLM prompts, featuring two tracks: Track A for multimodal hate detection in memes and Track B for harmful prompt detection in Arabic LLM safety evaluation. Fifty‑eight teams registered, 35 reached the final evaluation, and 27 submitted system‑description papers, with participants experimenting with models such as AraBERT, Jais, and Qwen3‑VL. The top systems achieved macro‑F1 scores of 0.823 on A1, 0.419 on A2, 0.984 on B1, and 0.790 on B2, with fine‑grained meme classification in A2 proving the most challenging due to sparse labels and distribution shifts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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