Researchers developed Atlas-Chat, a collection of LLMs for dialectal Arabic, focusing on Moroccan Arabic (Darija). They constructed an instruction dataset by consolidating existing Darija language resources and translating English instructions. Atlas-Chat models (2B, 9B, 27B) outperform state-of-the-art and Arabic-specialized LLMs like LLaMa, Jais, and AceGPT on Darija NLP tasks. Why it matters: This work addresses the gap in LLM support for low-resource Arabic dialects, providing a methodology for instruction-tuning and benchmarks for future research.
MBZUAI and École Polytechnique are deepening research collaboration through a Collaborative Research Agreement, focusing on large language models, foundation models for reasoning, and AI applications in biology, health, and AI safety. The partnership builds on a previous MoU and Scholars Exchange Program Agreement between the two institutions. MBZUAI's France Lab has developed Atlas-Chat, a family of open-source LLMs for the Moroccan Arabic dialect Darija, with models including Atlas-Chat-2B and Atlas-Chat-9B. Why it matters: This collaboration strengthens the AI ecosystems in both France and the UAE, fostering joint research efforts and supporting the next generation of AI researchers and innovators, with a specific focus on Arabic NLP.
This article discusses MBZUAI's efforts in advancing Arabic language AI, including the development of advanced linguistic models using deep learning techniques. Key initiatives include Jais, a 13B parameter Arabic LLM developed in collaboration with G42's Inception, and Atlas-Chat, which understands the Moroccan dialect. The university is also incorporating Arabic in practical AI solutions like BiMediX2, a healthcare multi-modal model that understands medical queries in both English and Arabic. Why it matters: These initiatives are crucial for preserving Arabic cultural heritage, enabling future discovery, and addressing linguistic challenges specific to the Arabic language in AI applications.