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arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2609.29349 (cs)
[Submitted on 24 Sep 2026]

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

View a PDF of the paper titled ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts, by Firoj Alam and 7 other authors
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Abstract:ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. In total, 58 teams registered, 35 participated in the final evaluation, and 27 submitted system-description papers. Participating teams explored models such as AraBERT, Jais, and Qwen3-VL. The best systems achieved macro-F1 scores of 0.823 on A1, 0.419 on A2, 0.984 on B1, and 0.790 on B2. Fine-grained meme classification in A2 was the most challenging setting, partly due to sparse labels and train-test distribution shifts.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.29349 [cs.CL]
  (or arXiv:2609.29349v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.29349
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ali Shahroor Ezzat [view email]
[v1] Thu, 24 Sep 2026 10:22:59 UTC (1,438 KB)
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