arXiv ·
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.
arXiv ·
The paper introduces a two-step approach for transliterating Judeo-Arabic text (written in Hebrew script) into Arabic script. The method involves character-level mapping followed by post-correction to fix grammatical and orthographic errors. The authors also benchmarked LLMs on the transliteration task and demonstrate that transliteration enables the use of Arabic NLP tools on Judeo-Arabic. Why it matters: This work makes Judeo-Arabic texts more accessible to Arabic NLP, enabling processing and analysis that was previously impossible.
arXiv ·
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.
MBZUAI ·
A delegation from the Abu Dhabi Executive Office (ADEO) Education Affairs Department visited MBZUAI on December 15, 2021. Ian Mathews, VP of Corporate Services, presented MBZUAI's progress and 2022 initiatives. Discussions covered the importance of collaboration and recruitment enhancements with ADEO's support. Why it matters: This visit highlights the ongoing relationship between MBZUAI and key Abu Dhabi government entities, signaling continued support for the university's AI initiatives.
KAUST ·
KAUST Ph.D. student Adel Bibi is researching how to bridge the gap between theory and practice in deep learning, focusing on the mathematical understanding of deep learning models. Bibi is currently interning at Intel in Munich and previously worked on various computer vision problems. He aims to use optimization and mathematics to better understand deep learning models and build better models systematically from theory. Why it matters: This research contributes to the fundamental understanding of deep learning, potentially leading to more efficient and reliable AI systems developed in the region.