Researchers have introduced BloomBench, a new cognitively human-grounded, bilingual (English-Arabic) multimodal benchmark for Vision-Language Models (VLMs), as part of the Almieyar benchmarking series. Grounded in Bloom's Taxonomy, it systematically evaluates six levels of cognition—Remember, Understand, Apply, Analyze, Evaluate, Create—through carefully designed image-question-answer tasks. A comprehensive study using BloomBench revealed that state-of-the-art VLMs exhibit strong semantic understanding but struggle significantly with factual recall and creative synthesis, alongside a critical performance gap between Arabic and English. Why it matters: This benchmark provides a crucial tool for diagnosing cognitive weaknesses in current VLMs and lays the groundwork for developing more cognitively aligned and inclusive multimodal AI, particularly for cross-lingual applications.
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.
Researchers introduce a benchmark to evaluate the factual recall and knowledge transferability of multilingual language models across 13 languages. The study reveals that language models often fail to transfer knowledge between languages, even when they possess the correct information in one language. The benchmark and evaluation framework are released to drive future research in multilingual knowledge transfer.
Researchers introduce Swan, a family of Arabic-centric embedding models including Swan-Small (based on ARBERTv2) and Swan-Large (based on ArMistral). They also propose ArabicMTEB, a benchmark suite for cross-lingual, multi-dialectal Arabic text embedding performance across 8 tasks and 94 datasets. Swan-Large achieves state-of-the-art results, outperforming Multilingual-E5-large in most Arabic tasks. Why it matters: The new models and benchmarks address a critical need for high-quality Arabic language models that are both dialectally and culturally aware, enabling more effective NLP applications in the region.
The paper introduces ALLaM, a series of large language models for Arabic and English, designed to support Arabic Language Technologies. The models are trained with language alignment and knowledge transfer in mind, using a decoder-only architecture. ALLaM achieves state-of-the-art results on Arabic benchmarks like MMLU Arabic and Arabic Exams. Why it matters: This work advances Arabic NLP by providing high-performing LLMs and demonstrating effective techniques for cross-lingual transfer learning and alignment with human preferences.
This paper introduces GigaBERT, a customized bilingual BERT model pre-trained for Arabic NLP and English-to-Arabic zero-shot transfer learning. The study evaluates GigaBERT's performance on four information extraction tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Results show that GigaBERT outperforms mBERT, XLM-RoBERTa, and AraBERT in both supervised and zero-shot transfer settings. Why it matters: GigaBERT advances Arabic NLP by providing a high-performing, publicly available model tailored for the complexities of the Arabic language and cross-lingual applications.
Researchers at MBZUAI have developed a new automatic method to examine cross-lingual abilities in multilingual language models, testing 10 models across 16 languages. They combined beam search with language-model-based simulation, generating 6,000 bilingual question pairs and found significant performance drops compared to English, even in high-resource languages like Chinese. The method introduces perturbations to test the models' ability to transfer knowledge rather than rely on memorization. Why it matters: This research highlights critical gaps in cross-lingual AI, providing a framework for developing more equitable and effective multilingual models, especially for Arabic and other under-represented languages.
This paper explores cross-lingual transfer in Arabic language models, which are typically pretrained on Modern Standard Arabic (MSA) but expected to generalize to diverse dialects. The study uses probing on 3 NLP tasks and representational similarity analysis to assess transfer effectiveness. Results show transfer is uneven across dialects, partially linked to geographic proximity, and models trained on all dialects exhibit negative interference. Why it matters: The findings highlight challenges in cross-lingual transfer for Arabic NLP and raise questions about dialect similarity for model training.