Stanford Professor Yoav Shoham, a leading AI expert, will speak at the MBZUAI Executive Program. Shoham will present on lingual cognition and intelligence as part of a virtual class session. He has founded several AI companies, including AI21 Labs, and chairs the AI Index initiative. Why it matters: The participation of globally recognized AI experts like Shoham enhances the prestige and educational value of AI programs in the UAE, attracting talent and fostering innovation.
ElevenLabs, a voice AI research and product company, presented at MBZUAI's Incubation and Entrepreneurship Center (IEC) on the adoption of audio AI in the Middle East. Hussein Makki, general manager for the Middle East at ElevenLabs, highlighted the potential of voice-native AI across sectors like telecommunications, banking, and education. ElevenLabs focuses on making content accessible and engaging across languages and voices through its text-to-speech models. Why it matters: This signals growing interest and investment in voice AI applications within the region, potentially transforming customer service and content accessibility in Arabic.
The paper introduces ArabianGPT, a suite of transformer-based language models designed specifically for Arabic, including versions with 0.1B and 0.3B parameters. A key component is the AraNizer tokenizer, tailored for Arabic script's morphology. Fine-tuning ArabianGPT-0.1B achieved 95% accuracy in sentiment analysis, up from 56% in the base model, and improved F1 scores in summarization. Why it matters: The models address the gap in native Arabic LLMs, offering better performance on Arabic NLP tasks through tailored architecture and tokenization.
G42's Core42 has released Jais, a new Arabic large language model. Jais includes 13 billion parameters and was trained on a dataset of 126B tokens, including 43B Arabic tokens. According to the developers, Jais achieves state-of-the-art results on Arabic benchmarks and competitive performance on English benchmarks. Why it matters: Jais represents a significant step forward for Arabic NLP, providing a powerful new tool for researchers and developers in the region.
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.
Researchers at MBZUAI have introduced QRAFT, an LLM-based framework designed to automate the generation of fact-checking articles. The system mimics the writing workflow of human fact-checkers, aiming to bridge the gap between automated fact-checking systems and public dissemination. While QRAFT outperforms existing text-generation methods, it still falls short of expert-written articles, highlighting areas for further research.