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.
Former Saudi Research Science Institute (SRSI) student Abdullatif, now a junior at Berkeley, published a paper in the Journal of the American Chemical Society (JACS). The paper, "Isomerically Pure Tetramethylrhodamine Voltage Reporters," details the design, synthesis, and application of Rhodamine Voltage Reporters (RhoVRs). Abdullatif, who worked at KAUST during her SRSI program on carbon dioxide capture, plans to return for advanced studies. Why it matters: This highlights KAUST's role in nurturing young Saudi talent in STEM and contributing to high-impact scientific research.
Adel Bibi, a KAUST alumnus and researcher at the University of Oxford, presented his research on AI safety, covering robustness, alignment, and fairness of LLMs. The research addresses challenges in AI systems, alignment issues, and fairness across languages in common tokenizers. Bibi's work includes instruction prefix tuning and its theoretical limitations towards alignment. Why it matters: This research from a leading researcher highlights the importance of addressing safety concerns in LLMs, particularly regarding alignment and fairness in the Arabic language.
Ahmad Alabdulghani, a KAUST master's student in Energy Resources and Petroleum Engineering, is studying fluid flow mechanisms in heterogeneous media under the supervision of Professor Hussein Hoteit. Alabdulghani is a member of the Advanced Reservoir Modeling and Simulation (ARMS) research group at ANPERC. He previously worked at Saudi Aramco's EXPEC Advanced Research Center and aims to pursue a doctorate at KAUST. Why it matters: This highlights KAUST's role in developing Saudi talent for the energy sector and fostering collaboration between academia and industry.
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.
MBZUAI PhD student Salem AlMarri, also a Dubai Police officer, has been appointed to the Dubai Youth Council for 2023-2025. AlMarri's research focuses on using AI to combat crime and improve emergency response times, aiming to contribute to the UAE's AI strategy. In 2019, he was recognized as one of the UAE’s up and coming scientist at the Her Highness Sheikha Fatima Bint Mubarak Program for Excellence and Community Intelligence. Why it matters: This appointment highlights the UAE's focus on empowering young talent in AI to drive innovation and address local challenges in public safety and smart city development.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.