This study evaluated the adversarial robustness of five state-of-the-art Arabic Language Models against various Arabic adversarial attacks at character, word, and sentence levels. It found that diacritic insertion could reduce model accuracy by up to 92%, while manipulating Arabic conjunctions led to a 58% accuracy degradation, and paraphrasing reduced performance by an average of 76%. While adversarial training improved overall resilience, particularly for MARBERT and AraBERT, challenges against character-level noise persist. Why it matters: These findings are crucial for understanding and mitigating security vulnerabilities in Arabic AI, guiding the development of more robust and safe Arabic NLP systems.
Researchers introduced CAMMAR (Culture-Aware Matryoshka for Metaphorical Arabic Representations), a framework designed to organize meaning in Arabic language models into nested lexical, cultural, and metaphorical embedding subspaces, addressing the issue of "semantic smearing." The framework, inspired by Al-Jurjani's theory of nazum, provides a training-free geometric measure of metaphoricity. Evaluated on a new span-annotated Arabic metaphor dataset, CAMMAR achieved an AUC of up to 0.84, effectively detecting metaphor when inter-layer geometry was shaped by paired supervision. Why it matters: This research offers a novel approach to enhancing the cultural and metaphorical understanding of Arabic AI, potentially leading to more nuanced and accurate Arabic language models.
A new paper evaluated twelve Small Language Models (SLMs) on Arabic natural language processing tasks, utilizing a benchmark of 240 Arabic test items across eight domains and ten language skills. The models were assessed in a zero-shot setting, with responses scored using a multi-model LLM-as-a-judge framework involving GPT-4.1 Mini, Claude Haiku 4.5, and DeepSeek-Chat. Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic, with results suggesting that strong Arabic alignment and instruction-following are crucial for performance. Why it matters: This benchmark offers a standardized method for evaluating compact Arabic language models, guiding future development towards more efficient, reliable, and culturally relevant Arabic AI systems.
This paper reflects on two decades of building NLP resources and research infrastructure for Arabic, an historically underserved language. The first decade focused on foundational linguistic infrastructure, while the second shifted towards computational social science and socially oriented applications. The authors highlight three lessons: dataset building is a social process, communities often matter more than shared tasks, and computational social science exposes challenges beyond traditional NLP training. Why it matters: The paper argues that the most difficult problems in developing NLP for underserved communities are social, institutional, and epistemic, offering critical insights for future research directions in Arabic AI.
RightNow-Arabic-0.5B-Turbo is a new 518M-parameter Arabic-specialized decoder LLM, built on Qwen2.5-0.5B, designed to bridge the gap between small multilingual and large Arabic-specialized models. Its development pipeline included adding 27,032 Arabic tokens via vocabulary injection, continued pretraining on 504M Arabic tokens, and fine-tuning with supervised instruction and direct preference optimization. The model achieved a 35.9% mean accuracy on three Arabic benchmarks (COPA-ar, Arabic HellaSwag, ArabicMMLU), outperforming all same-class open models and recovering 67% of SILMA-9B's mean accuracy at 1/18 the parameters, with all code and weights publicly released. Why it matters: This model significantly advances efficient Arabic NLP by providing a powerful, specialized sub-1B LLM suitable for edge deployment, making advanced Arabic AI more accessible and performant on resource-constrained devices.
TII in Abu Dhabi has launched Falcon Arabic, the first Arabic language model in the Falcon series, which is now the best-performing Arabic AI model in the region. They also released Falcon H1, a new model designed for performance and portability, outperforming Meta’s LLaMA and Alibaba’s Qwen in the small-to-medium size category. Falcon Arabic is built on Falcon 3-7B and trained on a high-quality native Arabic dataset. Why it matters: These releases strengthen the UAE's position as a leader in Arabic language AI and democratize access to high-performance AI models.
Abu Dhabi’s Technology Innovation Institute (TII) has launched Falcon-H1 Arabic, a new large language model based on a hybrid Mamba-Transformer architecture. The Falcon-H1 family comes in 3B, 7B, and 34B parameter sizes and outperforms existing models on the Open Arabic LLM Leaderboard (OALL). The model features improvements in data quality, dialect coverage, and long-context stability. Why it matters: This release strengthens the UAE's position in Arabic AI and provides a high-performing model tailored to the linguistic and cultural needs of the region.
Arabic Language Models (LMs) are primarily pretrained on Modern Standard Arabic (MSA), with an expectation of transferring to diverse Arabic dialects for real-world applications. This work explores cross-lingual transfer in Arabic LMs using probing on three Natural Language Processing (NLP) tasks and representational similarity. The findings indicate that transfer is possible but disproportionate across dialects, with some evidence of negative interference in models trained to support all Arabic dialects. Why it matters: This research highlights crucial challenges for building robust Arabic AI systems that effectively handle the significant linguistic diversity of the Arab world.