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.
Egyptian AI startup Intella, specializing in Arabic speech recognition, has raised $12.5 million in funding. The round was led by বিনিয়োগ, with participation from other investors. Intella plans to use the capital to expand its Arabic AI speech models and related services. Why it matters: The funding will help advance Arabic language AI capabilities, which are currently underserved compared to English-centric models.
The study analyzes over 1,000 images generated by ImageFX, DALL-E V3, and Grok for 56 Saudi professions, finding significant gender imbalances and cultural inaccuracies. DALL-E V3 exhibited the strongest gender stereotyping, with 96% male depictions, particularly in leadership and technical roles. The research underscores the need for diverse training data and culturally sensitive evaluation to ensure equitable AI outputs that accurately reflect Saudi Arabia's labor market and culture.
A new study compares vision-language models (VLMs) to YOLOv8 for wastewater treatment plant (WWTP) identification in satellite imagery across the MENA region. VLMs like Gemma-3 demonstrate superior zero-shot performance compared to YOLOv8, trained on a dataset of 83,566 satellite images from Egypt, Saudi Arabia, and UAE. The research suggests VLMs offer a scalable, annotation-free alternative for remote sensing of WWTPs.
The paper introduces Juhaina, a 9.24B parameter Arabic-English bilingual LLM trained with an 8,192 token context window. It identifies limitations in the Open Arabic LLM Leaderboard (OALL) and proposes a new benchmark, CamelEval, for more comprehensive evaluation. Juhaina outperforms models like Llama and Gemma in generating helpful Arabic responses and understanding cultural nuances. Why it matters: This culturally-aligned LLM and associated benchmark could significantly advance Arabic NLP and democratize AI access for Arabic speakers.
Researchers from MBZUAI have introduced VideoMolmo, a large multimodal model for spatio-temporal pointing conditioned on textual descriptions. The model incorporates a temporal module with an attention mechanism and a temporal mask fusion pipeline using SAM2 for improved coherence across video sequences. They also curated a dataset of 72k video-caption pairs and introduced VPoS-Bench, a benchmark for evaluating generalization across real-world scenarios, with code and models publicly available.