Large language models (LLMs) developed, deployed, and researched across the GCC — including Falcon (TII), Jais (MBZUAI/Inception), AceGPT (KAUST), ALLaM (SDAIA), and Fanar (QCRI).
The Human Phenotype Project (HPP), led by researchers from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), published findings in Nature Medicine detailing an understanding of the health-disease continuum. The HPP involves deep and longitudinal profiling of approximately 28,000 participants, collecting diverse data including medical history, lifestyle, blood tests, and molecular profiling. The project aims to create AI-based predictive models for disease onset and progression, and digital twins to simulate interventions. Why it matters: This research can transform precision medicine and preventative care in the UAE by creating personalized digital twins that can simulate interventions and predict health trajectories.
MBZUAI graduates Abdulla and Abdulrahman Almarzooqi are developing AI systems to improve UAE road safety. Abdulla's research focuses on external highway monitoring using MLLMs to analyze driving scenes and generate accident reports, while Abdulrahman's work uses in-cabin sensors to detect driver fatigue and distractions. Together, their systems aim to create a comprehensive view of factors influencing traffic accidents, with potential applications in ADAS and automated accident reporting. Why it matters: This research showcases the potential of AI agents and multimodal LLMs to proactively enhance road safety in the UAE and reduce traffic-related incidents.
MBZUAI researchers developed Mobile-VideoGPT, a compact and efficient multimodal model for real-time video understanding on edge devices. The system uses keyframe selection, efficient token projection, and a Qwen-2.5-0.5B language model. Testing showed that Mobile-VideoGPT is faster and performs better than other models while being significantly smaller, and the model and code are publicly available. Why it matters: This research enables on-device AI processing for video, reducing reliance on remote servers and addressing privacy concerns, which can accelerate the adoption of AI in mobile and embedded applications.
MBZUAI researchers developed FarSight, a plugin to reduce hallucinations in Multimodal Large Language Models (MLLMs). FarSight addresses the issue where MLLMs generate inaccurate text by losing focus on relevant image details, leading to snowball hallucinations. Testing on models like LLaVA-1.5-7B showed FarSight's effectiveness in reducing initial mistakes, thereby minimizing overall hallucinations. Why it matters: Improving the reliability of MLLMs is crucial for applications requiring high accuracy, enhancing their utility in various real-world scenarios.
MBZUAI has launched the Institute of Foundation Models (IFM) with a new Silicon Valley Lab in Sunnyvale, CA, joining existing facilities in Paris and Abu Dhabi. The launch event showcased PAN, a world model for simulating diverse realities with multimodal inputs. The IFM lab is also advancing K2-65B and JAIS AI systems. Why it matters: This expansion enhances MBZUAI's global presence and connects it with a critical AI ecosystem, supporting the UAE's economic diversification through advanced AI technologies.
MBZUAI is conducting research to improve cross-cultural understanding using AI, including studying LLM limitations in recognizing cultural references. They developed "Culturally Yours," a tool that helps users comprehend cultural references in text, and the "All Languages Matter Benchmark" (ALM Bench) to evaluate multimodal LLMs across 100 languages. MBZUAI has also developed LLMs tailored to low-resource languages like Jais (Arabic), Nanda (Hindi), and Sherkala (Kazakh). Why it matters: These initiatives promote inclusivity and ensure AI systems are culturally aware and can serve diverse populations effectively, particularly in the Middle East's multicultural context.
MBZUAI researchers presented FIRE, a new fact-checking framework for LLM outputs, at NAACL 2025. FIRE first assesses the LLM's confidence in its claims before searching the web, reducing computational cost. It also stores knowledge gained from web searches to aid in classifying other claims. Why it matters: This approach improves the efficiency and cost-effectiveness of automatically verifying the accuracy of LLMs, addressing a key limitation in their reliability.
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.
MBZUAI researchers have developed a new benchmark for evaluating the teaching abilities of large language models (LLMs), earning the SAC Award for Resources and Evaluation at NAACL 2025. The framework aims to measure how effectively LLMs can be used for personalized tutoring, addressing the "two sigma problem" in education. Unlike rule-based tutoring systems, LLMs offer fluency but lack pedagogical principles. Why it matters: This benchmark is a crucial step towards integrating learning science into AI, potentially enabling personalized AI tutors that significantly improve educational outcomes.
MBZUAI researchers developed FetalCLIP, an AI model trained on 210,000 ultrasound images for fast and reliable interpretation of fetal scans. MBZUAI's President Eric Xing contributed to the General Expression Transformer (GET), an AI foundation model acting as a biological simulator to predict gene behavior. MBZUAI and Carleton University created MedPromptX for quicker disease diagnosis and treatment plans using multimodal AI. Why it matters: These AI advancements from MBZUAI have the potential to revolutionize healthcare in the region and globally, from prenatal care to drug discovery and personalized medicine.
MBZUAI researchers created a new benchmark dataset called TextGames to evaluate the reasoning abilities of LLMs. The dataset uses simple, text-based games requiring skills like pattern recognition and logical thinking. LLMs struggled with the hardest questions, suggesting limitations in their reasoning capabilities despite advancements in language understanding. Why it matters: This research highlights the need for specialized reasoning models and benchmarks that go beyond memorization to truly test AI's problem-solving abilities.
MBZUAI researchers, in collaboration with over 70 researchers, have created the Culturally diverse Visual Question Answering (CVQA) benchmark to evaluate cultural understanding in multimodal LLMs. The CVQA dataset includes over 10,000 questions in 31 languages and 13 scripts, testing models on images of local dishes, personalities, and monuments. Testing of several multimodal LLMs on the CVQA benchmark revealed significant challenges, even for top models. Why it matters: This benchmark highlights the need for AI models to better understand diverse cultures, promoting fairness and relevance across different languages and regions.
MBZUAI-based startup LibrAI has launched the Libra-Leaderboard, an evaluation framework for LLMs that assesses both capability and safety. The leaderboard evaluates 26 mainstream LLMs using 57 datasets, assigning scores based on bias, misinformation, and oversensitivity. LibrAI also launched the Interactive Safety Arena to engage the public and educate them on AI safety through adversarial prompt testing. Why it matters: The Libra-Leaderboard provides a benchmark for responsible AI development, emphasizing the importance of aligning AI capabilities with safety considerations in the rapidly evolving LLM landscape.
The 31st International Conference on Computational Linguistics (COLING 2025) is being held in Abu Dhabi from January 18-24, hosted by MBZUAI. The conference features paper presentations, demonstrations, keynote speeches, workshops, and tutorials, with over 1,500 attendees. MBZUAI faculty and students contributed 22 papers to the conference, including research on fact-checking and cross-cultural content. Why it matters: Hosting COLING 2025 highlights the UAE's growing role as a hub for AI and NLP research, particularly in Arabic language processing.
MBZUAI researchers have released ALM Bench, a new benchmark dataset for evaluating the performance of multimodal LLMs on cultural visual question-answer tasks across 100 languages. The dataset includes over 22,000 question-answer pairs across 19 categories, with a focus on low-resource languages and cultural nuances, including three Arabic dialects. They tested 16 open- and closed-source multimodal LLMs on it, revealing a significant need for greater cultural and linguistic inclusivity. Why it matters: The benchmark aims to improve the inclusivity of multimodal AI systems by addressing the underrepresentation of low-resource languages and cultural contexts.
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.
MBZUAI researchers introduced Web2Code, a new dataset suite, at NeurIPS to enhance multimodal LLM performance in web page analysis and HTML generation. The suite includes a fine-tuning dataset and two benchmark datasets. Instruction tuning with Web2Code improved performance on specialized tasks without affecting general capabilities. Why it matters: This contribution addresses a key limitation in current multimodal LLMs, potentially boosting productivity in web design and development by providing targeted training data.
Researchers from MBZUAI and King's College London have developed a new prompting strategy called self-guided exploration to improve LLM performance on combinatorial problems. The method was tested on complex challenges like the traveling salesman problem. The findings will be presented at the 38th Annual Conference on Neural Information Processing Systems (NeurIPS) in Vancouver. Why it matters: This research could lead to practical applications of LLMs in industries like logistics, planning, and scheduling by offering new approaches to computationally complex problems.
Researchers at MBZUAI have developed LLM-DetectAIve, a tool to classify the degree of machine involvement in text generation. The system categorizes text into four types: human-written, machine-generated, machine-written and machine-humanized, and human-written and machine-polished. A demo website allows users to test the tool's ability to detect machine involvement. Why it matters: This research addresses the growing need to identify and classify AI-generated content in academic and professional settings, particularly in light of increasing LLM misuse.
A study co-authored by researchers from UC Berkeley, University of the Witwatersrand, Lelapa AI, and MBZUAI received the Outstanding Paper Award at EMNLP 2024. The paper critiques the term "low-resource" languages in NLP, highlighting its limitations in capturing the diverse challenges faced by different languages. The authors propose a more detailed analysis of resourcedness to encourage targeted support for languages currently underserved by technology. Why it matters: The research challenges assumptions in NLP and promotes more nuanced approaches to supporting the world's many languages, including Arabic, in AI systems.
MBZUAI doctoral student Mai A. Shaaban and colleagues developed MedPromptX, a system that analyzes chest X-rays and patient data to aid lung disease diagnoses. MedPromptX uses multimodal large language models with visual grounding and few-shot prompting, trained on a new dataset of 6,000 patient records (MedPromptX-VQA) derived from MIMIC-IV and MIMIC-CXR. The system addresses the challenge of incomplete electronic health records by leveraging the knowledge embedded in large language models to interpret lab results. Why it matters: This research advances AI-driven medical diagnostics by integrating diverse data sources and addressing data gaps, potentially leading to quicker and more accurate diagnoses.
MBZUAI researchers have created ArabicMMLU, the first benchmark dataset in Modern Standard Arabic for evaluating language understanding across multiple tasks. The dataset contains over 14,000 multiple-choice questions from school exams across the Arabic-speaking world and addresses the limitations of translated English datasets. It was presented at the 62nd Annual Meeting of the Association for Computational Linguistics in Bangkok. Why it matters: This benchmark enables a more accurate and culturally relevant evaluation of LLMs' capabilities in Arabic, which is crucial for developing AI tailored to the Arab world.
MBZUAI published over 300 papers at top-tier AI venues between January and June 2024, including 39 papers at ICLR 2024, building on 612 papers published in 2023. MBZUAI is now ranked among the world's top 100 universities in computer science and top 20 globally in AI-related fields. One standout paper, 'M4,' won the Best Resource Paper Award at EACL 2024 for its work on detecting LLM-generated text across multiple languages and domains. Why it matters: This continued research output and rising rankings solidify MBZUAI's position as a leading AI research institution in the GCC region and globally.
A study by MBZUAI's Preslav Nakov and Cornell co-authors examines how to develop systems that detect fake news in a landscape where text is generated by humans and machines. The research, presented at the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, analyzes fake news detectors' ability to identify human- and machine-written content. The study highlights biases in current detectors, which tend to classify machine-written news as fake and human-written news as true. Why it matters: Addressing these biases is crucial as machine-generated content becomes more prevalent in both real and fake news, requiring more nuanced detection methods.
MBZUAI M.Sc. graduate Mohammad Hanan Ghani developed new techniques to improve text-to-image generation from long text prompts, combining large language models and diffusion models. Advised by Dr. Salman Khan, Ghani published three papers at ICLR, BMVC, and NeurIPS, with the ICLR paper focusing on generating images that accurately reflect detailed text descriptions. The new system improves upon existing techniques to generate images that closely follow the details of the input text. Why it matters: This research addresses a key limitation in current T2I models and advances the field of multimodal AI, potentially improving the capabilities of robots and autonomous devices.
MBZUAI, Petuum, and LLM360 have launched K2-65B, an open-source 65B parameter LLM, trained on 1.4T tokens using 480 A100 GPUs. K2-65B outperforms Llama 2 70B while using 35% fewer resources, emphasizing sustainable AI development. The model and its chat variant, K2-Chat, excel in math, coding, medicine, and human-like response generation, with the model available under the Apache 2.0 license. Why it matters: This launch highlights the UAE's increasing capabilities in developing efficient and high-performing LLMs, promoting open-source collaboration and setting new standards for sustainable AI practices in the region.
MBZUAI's Institute of Foundation Models (IFM) has launched five new specialized language and multimodal models, including BiMediX, PALO, GLaMM, GeoChat, and MobiLLaMA. These models address real-world applications in healthcare, visual reasoning, multilingual capabilities, geospatial analysis, and mobile device efficiency. BiMediX is a bilingual medical LLM, while GLaMM generates natural language responses related to objects in an image at the pixel level. Why it matters: This launch demonstrates MBZUAI's commitment to advancing AI research and developing practical AI solutions for various industries, especially with a focus on Arabic language capabilities.
MBZUAI increased faculty diversity and worked with global partners on application projects in 2023, including developing Jais (with Core42 and Cerebras) and Vicuna (with UC San Diego, UC Berkeley, CMU, and Stanford). They also launched Jais Climate, a bilingual LLM for climate intelligence, and LLM360, a framework for transparent LLM research. Why it matters: MBZUAI's involvement in open-source GenAI initiatives and partnerships positions the UAE as a key player in responsible AI development and talent creation.
MBZUAI researchers are developing spiking neural networks (SNNs) to emulate the energy efficiency of the human brain. Traditional deep learning models like those powering ChatGPT consume significant energy, with a single query using 3.96 watts. SNNs aim to mimic biological neurons more closely to reduce energy consumption, as the human brain uses only a fraction of the energy compared to these models. Why it matters: This research could lead to more sustainable and energy-efficient AI technologies, addressing a major challenge in deploying large-scale AI systems.
MBZUAI faculty and students will present 44 papers at the Empirical Methods in Natural Language Processing (EMNLP) conference in Singapore. Research topics include disinformation detection, social media analysis, dialogue generation, and Arabic LLMs. Preslav Nakov, Iryna Gurevych, Timothy Baldwin, Alham Fikri Aji, and Muhammad Abdul-Mageed are among the MBZUAI researchers presenting at the conference. Why it matters: MBZUAI's strong presence at a top NLP conference highlights the UAE's growing contributions to cutting-edge AI research and its increasing global prominence in the field.
An MBZUAI team won the best paper award at the inaugural Arabic Natural Language Processing Conference for their work on processing Arabic speech. Their study establishes a new approach to tackle the complexities of spoken Arabic, which differs significantly from text-based language models. The team's approach aims to advance new tools for Arabic speakers by addressing challenges like intonation and the continuous nature of speech. Why it matters: This award highlights the importance of specialized research in Arabic NLP, as mainstream LLMs often face limitations in accurately processing the nuances of Arabic speech.
MBZUAI researchers created Bactrian-X, a new dataset to improve LLM instruction following in low-resource languages. The dataset leverages instruction tuning, pairing instructions in various languages with expected responses. Bactrian-X builds upon existing open-source instruction tuning models. Why it matters: This work aims to democratize access to LLMs by enabling users to interact with them in their native languages, even when English proficiency is limited.
MBZUAI and Core42 have launched Jais Climate, the first bilingual (Arabic/English) LLM dedicated to climate and sustainability. It is fine-tuned with 1.4 million climate-related instructions and trained on the ClimaInstruct dataset. Jais Climate is built on Jais 13B and incorporates technology from Vicuna, an open-source LLM. Why it matters: This model provides accessible climate data to a wide audience, including decision-makers and the general public, and highlights the UAE's focus on using AI for sustainability.
MBZUAI faculty and students will present 53 papers at NeurIPS 2023. Key faculty include Eric Xing, Kun Zhang, and Tongliang Liu, each contributing to nine papers. One paper was selected for oral presentation and two for spotlight presentations. Why it matters: MBZUAI's strong presence at NeurIPS highlights its growing influence in the global AI research community and its focus on high-impact AI research.
MBZUAI launched the Institute of Foundation Models, the MBZUAI Metaverse Center (MMC), and the MBZUAI Incubation and Entrepreneurship Center (MIEC) at GITEX 2023. The university also signed MoUs with Abu Dhabi Customs and the Department of Municipalities and Transport to collaborate on AI applications. MBZUAI faculty participated in panel discussions on generative AI and large language models, including the Jais Arabic LLM. Why it matters: These initiatives and partnerships signal MBZUAI's central role in advancing AI research, development, and adoption across key sectors in the UAE.
MBZUAI has launched the Institute of Foundation Models to advance generative AI research and development. The institute will focus on developing large-scale AI models adaptable to various applications, building on MBZUAI's existing work with models like Jais, Llama 2, and Vicuna. It will focus on multi-modal foundation models applicable to areas like healthcare, finance and environmental engineering. Why it matters: This initiative further solidifies the UAE's position as a leader in AI, particularly in the development and application of foundation models for diverse industries.
MBZUAI is a global partner in Meta's release of Llama 2, joining organizations like IBM, AWS, Microsoft, and NVIDIA. MBZUAI will provide early feedback and help build the software as a global community. MBZUAI is working on large language models, developing a sustainable LLM named Vicuna, and strengthening infrastructure for LLM-chat evaluation. Why it matters: MBZUAI's involvement promises to bring about a new generation of UAE-born AI advancements built around the Llama 2 ecosystem and fact-checking capabilities.
G42's Inception has open-sourced Jais, a 13-billion parameter Arabic large language model (LLM). Jais was trained on a 395-billion-token Arabic and English dataset and outperforms existing Arabic models. The model is a collaboration between Inception, MBZUAI, and Cerebras Systems, and was trained on the Condor Galaxy supercomputer. Why it matters: This release establishes a new standard for Arabic language AI, providing over 400 million Arabic speakers access to generative AI and fostering innovation in the region.
Researchers from MBZUAI, University of British Columbia, and Monash University have created LaMini-LM, a collection of small language models distilled from ChatGPT. LaMini-LM is trained on a dataset of 2.58M instructions and can be deployed on consumer laptops and mobile devices. The smaller models perform almost as well as larger counterparts while addressing security concerns. Why it matters: This work enables the deployment of LLMs in resource-constrained environments and enhances data security by reducing reliance on cloud-based LLMs.
Researchers from MBZUAI, UC Berkeley, CMU, Stanford, and UC San Diego collaborated to create Vicuna, an open-source chatbot that costs $300 to train, unlike ChatGPT which costs over $4 million. Vicuna achieves 90% of ChatGPT's subjective language quality while being far more energy-efficient and can run on a single GPU. It was fine-tuned from Meta AI’s LLaMA model using user-shared conversations and has gained significant traction on GitHub. Why it matters: This research demonstrates that high-quality chatbots can be developed at a fraction of the cost and environmental impact, opening up new possibilities for sustainable AI development in the region.
MBZUAI researchers had 26 papers accepted at ACL 2023, a top NLP conference. Assistant Professor Alham Fikri Aji co-authored eight papers, including one on crosslingual generalization through multitask finetuning (MTF). Deputy Department Chair Preslav Nakov co-authored a paper on a Bulgarian language understanding benchmark dedicated to the memory of Yale Computer Scientist Dragomir R. Radev. Why it matters: MBZUAI's strong presence at ACL highlights its growing influence in the NLP field and its contributions to multilingual AI research.
MBZUAI President Eric Xing led a global collaboration to develop Vicuna, an LLM alternative to GPT-3 addressing the unsustainable costs of training LLMs. OpenAI CEO Sam Altman acknowledged Abu Dhabi's role in the global AI conversation, building off of achievements like Vicuna. Xing and colleagues are publishing research at MLSys 2023 on "cross-mesh resharding" to improve computer communication in deep learning, aiming for low-carbon, affordable, and miniaturized AI. Why it matters: This research signals a push towards sustainable AI development in the region, emphasizing efficiency and reduced environmental impact.
A new paper from MBZUAI researchers explores using ChatGPT to combat the spread of fake news. The researchers, including Preslav Nakov and Liangming Pan, demonstrate that ChatGPT can be used to fact-check published information. Their paper, "Fact-Checking Complex Claims with Program-Guided Reasoning," was accepted at ACL 2023. Why it matters: This research highlights the potential of large language models to address the growing challenge of misinformation, with implications for maintaining information integrity in the digital age.
MBZUAI had 22 papers accepted at ICLR 2023, with faculty Kun Zhang co-authoring seven of them. Yuanzhi Li, an affiliated assistant professor at MBZUAI, received an honorable mention for his paper on knowledge distillation. Additionally, a paper co-authored by MBZUAI President Eric Xing was recognized as a top 5% paper at the conference. Why it matters: MBZUAI's strong presence at a top-tier machine learning conference like ICLR demonstrates the university's growing influence and research capabilities in the global AI landscape.
Michael I. Jordan, a UC Berkeley Distinguished Professor and influential figure in machine learning and AI, has been appointed laureate professor at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He will also serve as honorary director of MBZUAI's new Laureate Faculty Program. The program aims to attract leading researchers to MBZUAI by providing resources and minimizing administrative burdens. Why it matters: This appointment strengthens MBZUAI's reputation and enhances the UAE's AI ecosystem by bringing in a world-renowned AI expert to foster research and innovation.
G42 has launched Nanda 87B, an open-source Hindi-English LLM developed by MBZUAI in collaboration with Inception and Cerebras. Nanda 87B is built upon Llama-3.1 70B and trained on a dataset with over 65 billion Hindi tokens. The model is engineered for real-world use being fluent in formal Hindi, casual speech, and Hinglish, and is designed for translation, summarization, instruction-following, and transliteration tasks. Why it matters: This release marks a major advancement in creating inclusive AI technology tailored for one of the world's largest linguistic communities.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.
The article from The National discusses the potential challenges and risks for employers when their staff utilize generative AI tools such as ChatGPT, Claude, and Gemini for work-related tasks. These risks typically include concerns around data privacy, intellectual property leakage, and the accuracy and reliability of AI-generated content. It emphasizes the need for organizations to establish clear policies and guidelines regarding the use of such tools to safeguard sensitive information and maintain compliance. Why it matters: This analysis highlights critical governance and operational considerations for businesses and policymakers in the Middle East as AI adoption continues to grow across various industries.
A recent survey conducted by JetBrains revealed that 90% of developers are currently using AI coding agents in their professional work. This finding highlights a significant integration of artificial intelligence tools into the daily workflow of software development. The survey data indicates a widespread adoption of AI-powered assistance among the global developer community. Why it matters: This trend signifies a critical evolution in software development practices that will impact productivity, skill requirements, and infrastructure demands across the Middle East's tech industry.
This paper presents an automated ArcGIS Pro tool using ArcPy to extract Land Surface Temperature (LST) from Landsat 7, 8, and 9 satellite datasets. The tool automates multi-step workflows like radiometric calibration and emissivity correction, reducing processing time from 11-58 minutes manually to 4-11 minutes. It was successfully tested with data from Ras Al Khaimah (RAK) in the UAE, demonstrating accurate LST mapping compatible with local weather conditions, and includes an optional Large Language Model (LLM) for statistical result interpretation and reporting. Why it matters: This tool offers a practical application of AI and automation for environmental monitoring in the UAE, significantly improving efficiency and accuracy in remote sensing for climate research and urban planning.