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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.
arXiv:2605. 03226v2 Announce Type: replace-cross Abstract: Safety fine-tuning of language models typically requires a curated adversarial dataset.
arXiv:2610.33813v1 Announce Type: new Abstract: Fine-tuning a language model for classification involves many design decisions. Comparisons of low-rank adaptation (LoRA) with full fine-tuning are com...
arXiv:2608.21880v1 Announce Type: new Abstract: Bangla large language model (LLM) safety is difficult to evaluate with English-centric or standard-script benchmarks because Bangla users routinely wri...
arXiv:2608.01291v2 Announce Type: replace Abstract: We present ArabicDialectSafety, a human-curated Arabic safety dataset of 25,071 prompts covering six Arabic varieties: Modern Standard Arabic, Syri...
arXiv:2607. 02079v1 Announce Type: cross Abstract: We present HaloGuard 1.