The Hala technical report introduces a family of Arabic-centric instruction and translation models developed using a translate-and-tune pipeline. A strong Arabic-English teacher model is compressed to FP8 and used to create bilingual supervision data. The LFM2-1.2B model is fine-tuned on this data and used to translate English instruction sets into Arabic, creating a million-scale corpus. Why it matters: The release of models, data, evaluation tools, and recipes will accelerate research and development in Arabic NLP, providing valuable resources for the community.
Princess Hala bint Khalid bin Sultan discussed the Khaled bin Sultan Living Oceans Foundation's marine preservation work at KAUST's Enrichment in the Fall program. The foundation focuses on research, education, and communication to preserve marine environments locally, regionally, and globally. Key projects include a five-year research expedition across 15 countries and the Mangroves Program in Jamaican and Bahamian schools. Why it matters: This highlights the ongoing efforts and commitment within Saudi Arabia to address critical environmental challenges in marine ecosystems through research and education.
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
This article discusses a talk by Houda Elmimouni on designing technology with user values in mind, using privacy and robotic telepresence research as examples. The first study examines privacy practices, while the second focuses on values in robotic telepresence in classrooms. Elmimouni highlights the importance of aligning technology design with social values like privacy. Why it matters: The emphasis on user-centered design and social values provides insights applicable to AI development in the Middle East, where cultural context and ethical considerations are paramount.
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.
The TAQADAM University Entrepreneur Accelerator program held a showcase at KAUST featuring 13 Saudi university startup teams. The program, sponsored by the Saudi British Bank (SABB), aims to develop early-stage entrepreneurs into high-potential startups. The overall winner was Telaa, offering an anti-corrosion coating using recycled crumb rubber. Why it matters: This multi-university accelerator boosts Saudi Arabia's Vision 2030 by fostering innovation and supporting the SME sector, providing crucial seed funding and mentorship for young entrepreneurs.