Natural language processing research from GCC institutions, covering Arabic NLP, multilingual models, text classification, named entity recognition, and machine translation.
KAUST researchers are exploring the link between nutrition and brain-energy metabolism to address cognitive decline, dementia, and Alzheimer’s disease. Dr. Pierre Magistretti and Dr. Johannes le Coutre are collaborating on ways to merge brain-energy metabolism research into the field of nutrition. They published an article entitled “Goals in Nutrition Science 2015-2020” in the journal Frontiers in Nutrition. Why it matters: This research could lead to nutritional interventions to hinder or prevent cognitive decline, offering a new approach beyond traditional drug treatments.
Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
This paper introduces a novel dataset for Arabic handwritten isolated alphabet letters to serve as a benchmark for future research. The study presents a comparative evaluation of window-based descriptors for Arabic handwritten alphabet recognition, testing different descriptors with various classifiers. The experiments demonstrate that window-based descriptors perform well, especially when combined with a novel spatial pyramid partitioning scheme. Why it matters: The new dataset and analysis of descriptors will help advance Arabic OCR and handwritten text recognition systems.
KAUST scientists are developing models to predict extreme weather events like the 2009 Jeddah flood, which caused significant damage. Prof. Ibrahim Hoteit's team is using data from satellites, international sources, and local entities like PME and the Jeddah Municipality to build high-resolution models. The aim is to improve predictions of extreme rain events by one or two days and issue timely warnings. Why it matters: Improving extreme weather prediction is crucial for mitigating the impact of climate change in vulnerable regions like the GCC.
KAUST's Environmental Epigenetics Program (KEEP), led by Prof. Valerio Orlando, focuses on understanding how cells acquire and maintain memory, particularly in response to environmental factors. The research investigates the role of non-coding RNA and chromosomal components in regulating gene expression beyond the DNA sequence. Epigenetics explains how the same genome can be interpreted differently, allowing cells and organs to adapt to changing conditions. Why it matters: This research could provide insights into how environmental factors impact gene expression and cell function, potentially leading to advances in understanding and treating diseases.
KAUST professors Samir Hamdan and Nina Fedoroff collaborated on research published in Nucleic Acids Research focusing on microRNA (miRNA) biogenesis in plants. The study examined miRNA production in Arabidopsis thaliana and found that the protein SERRATE (SE) is integral to the processing of pri-miRNA by DCL1. They characterized the interactions of SE with RNA and DCL1, elucidating the mechanism by which SE promotes DCL1 activity. Why it matters: Understanding miRNA biogenesis could help modify crop plants to better tolerate stressful conditions, potentially increasing crop yields and productivity in the region.
This paper introduces a new non-statistical Arabic lemmatizer algorithm designed for information retrieval systems. The lemmatizer leverages Arabic language knowledge resources to generate accurate lemma forms and relevant features. The algorithm achieves a maximum accuracy of 94.8% and 89.15% on first seen documents, outperforming the Stanford Arabic model's 76.7% on the same dataset. Why it matters: Accurate Arabic lemmatization is crucial for improving the performance of Arabic information retrieval systems, which can enhance access to Arabic language content.
Researchers created a cross-cultural corpus of annotated verbal and nonverbal behaviors in receptionist interactions. The corpus includes native speakers of American English and Arabic role-playing scenarios at university reception desks in Doha, Qatar, and Pittsburgh, USA. The manually annotated nonverbal behaviors include gaze direction, hand gestures, torso positions, and facial expressions. Why it matters: This resource can be valuable for the human-robot interaction community, especially for building culturally aware AI systems.
The paper introduces a web-based expert system called RCSES for civil service regulations in Saudi Arabia. The system covers 17 regulations and utilizes XML for knowledge representation and ASP.net for rule-based inference. RCSES was validated by domain experts and technical users, and compared favorably to other web-based expert systems.
MBZUAI launched a report titled "AI for the Global South: 12 Critical Research Questions for the Next Decade" at the India AI Impact Summit. The report identifies 12 urgent research priorities related to AI's accessibility and benefits for developing countries. It grew out of MBZUAI’s AI for the Global South (AI4GS) workshop in December 2023, co-organized with IIT Delhi Abu Dhabi and sponsored by Microsoft. Why it matters: The report addresses the underrepresentation of local data, languages, and institutions from the Global South in modern AI systems, aiming to guide AI development towards inclusive and locally grounded systems.
MBZUAI researchers developed LetBabyTalk, an AI-powered multilingual parenting app that analyzes baby cries to identify needs like hunger or sleepiness. The app is trained on over 1,000 baby cries and uses supervised machine learning with input from experienced parents and educators. Cradle AI, the startup behind the app, aims to bridge the gap between advanced AI research and real-world solutions, focusing on family care and education. Why it matters: This project demonstrates the potential of AI to address everyday challenges and improve the lives of families in the region and globally, while also showcasing MBZUAI's focus on AI for social good.
MBZUAI is hosting the AI for the Global South (AI4GS) workshop in collaboration with the Indian Institute of Technology Delhi Abu Dhabi. The workshop aims to address the underrepresentation of the Global South in AI development and ensure AI benefits everyone. It brings together researchers from diverse disciplines and geographies, including representatives from NGOs, technology companies like Microsoft, Google, Cohere, and G42, and startups. Why it matters: The initiative promotes inclusive AI development, ensuring that AI tools and research consider the needs and contexts of underrepresented regions.
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.
MBZUAI researchers developed a symbolic adversarial learning framework (SALF) for fake news detection using LLM-powered agents. SALF employs a generator and a detector in a debate-like setup, judged by another LLM, to improve the agents' ability to create and identify fake news. Testing showed that the SALF generator degraded the performance of existing fake news detectors by 53.4% on Chinese and 34.2% on English datasets. Why it matters: This research offers a novel approach to combating the evolving threat of LLM-generated disinformation, a critical issue for maintaining reliable information ecosystems in the region and globally.
MBZUAI recognized seven faculty members for outstanding contributions in research, teaching, and mentorship at its annual Faculty Recognition and Welcome Reception. Associate Professor Salman Khan received the Distinguished Research Award for his work on multimodal models for remote Earth observation, including projects like AI4Weather and the AI Global Agriculture Advisory. Assistant Professor Alham Fikri Aji received the Early Career Researcher Award for his contributions to low-resource NLP and international collaborations. Why it matters: The awards highlight MBZUAI's focus on advancing AI for global challenges and recognizing faculty contributions to research and education.
The International Olympiad in AI (IOAI), a competition for high school students in NLP, CV, and robotics, will be hosted in Abu Dhabi by MBZUAI in 2026. MBZUAI faculty members Yova Kementchedjhieva and Maxim Panov are part of IOAI's International Scientific Committee and helped bring the event to the UAE. The IOAI 2025 in Beijing included individual contests, team challenges, and the GAITE program, testing participants with AI tasks and generalization challenges. Why it matters: Hosting IOAI in Abu Dhabi highlights the UAE's commitment to fostering AI talent and positions MBZUAI as a key player in global AI education.
MBZUAI master's graduate Rohit Bharadwaj is pursuing a Ph.D. at the University of Edinburgh, following in the footsteps of Geoffrey Hinton. His research focuses on developing generative models, specifically diffusion models, to anonymize datasets while preserving utility, addressing GDPR compliance. He aims to balance privacy protection with the need for useful data in AI systems. Why it matters: This highlights the growing importance of MBZUAI as a feeder institution for top global AI research programs and the increasing focus on privacy-preserving AI technologies.
MBZUAI concluded its 2025 Undergraduate Research Internship Program (UGRIP), hosting 57 students from 24 countries. The four-week program, held in Abu Dhabi, allowed undergraduates to collaborate with faculty on AI research projects in machine learning, computer vision, and NLP. The 2025 edition had a 4% acceptance rate, with nearly 2,000 applications received. Why it matters: The program highlights MBZUAI's role in attracting global AI talent to the UAE and fostering research collaborations.
MBZUAI researchers presented a study at ACL 2024 on improving Arabic ASR by pre-training on dialectal Arabic. They trained three versions of the ArTST model: one on MSA, one on MSA and dialectal data, and one on MSA, dialectal, and multilingual data. Results showed that pre-training on dialectal Arabic improves ASR performance across MSA and various dialects. Why it matters: This research addresses a key challenge in Arabic NLP, given the diversity and lack of standardization in dialects, which could lead to more accurate speech recognition systems.
MBZUAI researchers identified 'self-referencing causal cycles' in LLM training data that can mitigate the 'reversal curse,' where LLMs struggle with information presented in reverse order. The study, to be presented at ACL, explains that the transformer architecture's unidirectional token generation causes this issue. By leveraging the repetitive nature of information in training texts, the team developed an efficient solution to improve LLM performance. Why it matters: Overcoming the reversal curse can significantly enhance LLM accuracy and reliability, especially in tasks requiring bidirectional reasoning and understanding of context.
MBZUAI NLP master's graduate Hasan Iqbal developed OpenFactCheck, a framework for fact-checking and evaluating the factual accuracy of large language models. The framework consists of three modules: ResponseEvaluator, LLMEvaluator, and CheckerEvaluator. OpenFactCheck was published at EMNLP 2024 and accepted at NAACL 2025 and COLING 2025, with Iqbal playing an active role at COLING in Abu Dhabi. Why it matters: The development of automated fact-checking frameworks is crucial for ensuring the reliability and trustworthiness of information generated by increasingly prevalent LLMs, especially in the Arabic-speaking world.
MBZUAI doctoral student Hawau Toyin is applying AI to the identification, correction, and evaluation of stuttering, particularly in developing countries where it often goes undiagnosed. She is collaborating with the SpeechCare Center UAE and her advisor Dr. Hanan Aldarmaki to develop AI tools for faster and more accessible diagnosis and treatment. The research focuses on data collection from around the world to build an effective AI system that can analyze the various forms of stuttering. Why it matters: This research addresses a critical healthcare gap by leveraging AI to improve diagnosis and treatment of speech disorders in underserved regions.
Muhammad Arslan Manzoor became MBZUAI's first NLP Ph.D. graduate, focusing his research on media bias under Professor Preslav Nakov. His thesis, 'MGM,' explored using audience overlap graphs to predict the factuality and bias of news media, an approach that differs from traditional textual analysis. Manzoor's work aims to improve the efficiency of media profiling in real-time by leveraging relationships captured in media graphs. Why it matters: This research offers innovative methods for identifying bias in news, which is crucial for promoting informed social discourse and combating disinformation in the region.
MBZUAI valedictorian Salma Hassan developed a multimodal graph learning approach for early dementia diagnosis and prognosis. Her master's thesis focused on differential diagnosis to identify specific dementia subtypes and predicting cognitive decline in preclinical patients. The AI model leverages brain imaging, genetics, and clinical records to improve diagnostic accuracy. Why it matters: This research can improve early detection and resource allocation for dementia management, especially in developing countries.
MBZUAI researchers presented a new causal representation learning framework at ICLR for identifying latent causal variables in multimodal biological data. The framework addresses the challenge of uncovering underlying causal factors from lab tests, genetic information, and medical images. The new approach can identify latent causal variables and their influence on observed biological outcomes across modalities. Why it matters: The model's ability to analyze causal mechanisms between modalities can lead to more complete insights in biomedical research.
NYU Abu Dhabi and MBZUAI researchers have developed ARWI, a free web application to help Arabic language learners improve their writing skills in Modern Standard Arabic. ARWI provides essay prompts aligned with CEFR skill levels, features an Arabic text editor, and gives personalized feedback. The tool won the Diversity Award at the Workshop on Intelligent and Interactive Writing Assistants (In2Writing). Why it matters: This tool can help preserve the quality and personal voice of Arabic writing amid the rise of LLMs.
MBZUAI Professor Preslav Nakov discusses Meta's shift to crowdsourced fact-checking via Community Notes, replacing third-party fact-checkers. Community Notes, originating from Twitter's Birdwatch, allows users to add context to potentially misleading posts, visible after community consensus. Research indicates this approach can reduce misinformation and lead to post retractions. Why it matters: The adoption of crowdsourcing for content moderation by major platforms like Meta could significantly impact online information quality for billions of users.
Researchers at MBZUAI presented a new Arabic dataset at NAACL to measure LLM safety, building on a Chinese dataset called 'Do Not Answer'. The dataset includes nearly 5,800 questions with challenges and harmless requests containing sensitive terms to test for over-sensitivity. The team localized cultural concepts and added 3,000 questions specific to Arabic language and culture. Why it matters: This comprehensive benchmark, accounting for the diversity of Arabic dialects and cultures, advances the development of safer and more culturally aligned LLMs for Arabic speakers.
MBZUAI researchers presented two studies at NAACL 2025 concerning how LLMs understand cultural differences, with one study winning the SAC award. One study, titled "Reading between the lines: Can LLMs identify cross-cultural communication gaps," assesses GPT-4o's ability to identify cultural references in Goodreads book reviews. The researchers created a benchmark dataset using annotations from 50 evaluators across different cultures to measure the LLM's ability to identify culture-specific items (CSIs). Why it matters: Improving LLMs' cross-cultural understanding is crucial for ensuring these models can be used effectively and equitably across diverse global contexts.
MBZUAI researchers found that only 5.7% of music in existing datasets used to train generative music systems comes from non-Western genres. They discovered that 94% of the music represented Western music, while Africa, the Middle East, and South Asia accounted for only 0.3%, 0.4%, and 0.9% respectively. The team also tested whether parameter-efficient fine-tuning with adapters could improve generative music systems on underrepresented styles, presenting their findings at NAACL. Why it matters: This research highlights the critical need for more diverse datasets in AI music generation to better serve global musical traditions and audiences.
MBZUAI researchers developed AnomalyGen, a framework using foundation models to help household robots anticipate and react to dangerous scenarios. The system uses collaborative agents to brainstorm hazards, recreates scenarios in a 3D simulation, and develops mitigation methods. AnomalyGen will be presented at the upcoming NAACL conference. Why it matters: This research advances the development of trustworthy AI for real-world applications, specifically enabling robots to proactively ensure safety in home environments.
MBZUAI graduate Svetlana Maslenkova worked with Assistant Professor Mohammad Yaqub on a project focused on the earlier detection of kidney failure using tabular data. Maslenkova's master's thesis involved predicting Acute Kidney Injury (AKI) using Electronic Health Records (EHR), specifically the MIMIC-IV v2.0 database. She found that patient weight distribution was a factor in the severity of kidney failure. Why it matters: This research highlights the potential of AI and machine learning to improve healthcare outcomes through the analysis of often-overlooked tabular data in electronic health records.
MBZUAI researchers have developed "Culturally Yours," a reading assistant that highlights and explains culturally-specific items on webpages to help users understand unfamiliar terms. The tool addresses the "cold-start problem" by asking users for demographic information to personalize the identification of potentially unfamiliar cultural references. It was presented at the 31st International Conference on Computational Linguistics in Abu Dhabi. Why it matters: This tool can help bridge linguistic and cultural gaps, particularly for underrepresented languages and cultures, and aid businesses in reaching diverse audiences.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
Caltech graduate student Surya Narayanan Hari presented his research on replicating human-like memory in machines at MBZUAI. He discussed how the thalamus, which filters sensory and motor signals in the brain, inspires the development of routed monolithic models in AI. Hari explained that memory retrieval occurs on object, embedding, and circuit levels in the human brain. Why it matters: This talk highlights the potential of neuroscience-inspired AI architectures for improving memory and information processing in AI systems, which could accelerate the development of more efficient and context-aware AI models in the region.
MBZUAI hosted a talk by Visiting Associate Professor Adrian Bors on continuously streaming AI and the challenge of catastrophic forgetting. The talk covered approaches to continual learning like expanding mixtures of models and generative replay mechanisms. Results were presented on image classification and generation tasks. Why it matters: Continual learning is crucial for AI systems to adapt to new environments and real-world data without forgetting previous knowledge.
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
Giovanni Puccetti from ISTI-CNR presented research on linguistic probing of language models like BERT and RoBERTa. The research investigates the ability of these models to encode linguistic properties, linking this ability to outlier parameters. Preliminary work on fine-tuning LLMs in Italian and detecting synthetic news generation was also presented. Why it matters: Understanding the inner workings and linguistic capabilities of LLMs is crucial for improving their reliability and adapting them to diverse languages like Arabic.
Nadine El Naggar from City, University of London presented research on RNN learning of counting behavior, formalizing it as Dyck-1 acceptance. Empirically, RNN models struggle to learn exact counting and fail on longer sequences, even when weights are correctly initialized. Theoretically, Counter Indicator Conditions (CICs) were proposed and proven necessary/sufficient for exact counting in single-cell RNNs, but experiments show these CICs are not found or are unlearned during training. Why it matters: This work highlights challenges in RNNs learning systematic tasks, suggesting gradient descent-based optimization may not achieve exact counting behavior with standard setups.
Niket Tandon from the Allen Institute for AI presented a talk at MBZUAI on enabling large language models to focus on human needs and continuously learn from interactions. He proposed a memory architecture inspired by the theory of recursive reminding to guide models in avoiding past errors. The talk addressed who to ask, what to ask, when to ask and how to apply the obtained guidance. Why it matters: The research explores how to align LLMs with human feedback, a key challenge for practical and ethical AI deployment.
Mausam, head of Yardi School of AI at IIT Delhi and affiliate professor at University of Washington, will discuss Neuro-Symbolic AI. The talk will cover recent research threads with applications in NLP, probabilistic decision-making, and constraint satisfaction. Mausam's research explores neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and intelligent information systems. Why it matters: Neuro-Symbolic AI is gaining importance as it combines the strengths of neural and symbolic approaches, potentially leading to more robust and explainable AI systems.
Thamar Solorio from the University of Houston presented preliminary work on multimodal representation learning for detecting objectionable content in videos at MBZUAI. The research investigates two multimodal pretraining mechanisms, finding contrastive learning more effective than unimodal representation prediction. The study also assesses the value of common multimodal corpora for this task. Why it matters: This research contributes to the development of AI techniques for content moderation, an important issue for online platforms in the Middle East and globally.
A recent talk at MBZUAI discussed "Green Learning" and Operational Neural Networks (ONNs) as efficient alternatives to CNNs. ONNs use "nodal" and "pool" operators and "generative neurons" to expand neuron learning capacity. Moncef Gabbouj from Tampere University presented Self-Organized ONNs (Self-ONNs) and their signal processing applications. Why it matters: Exploring more efficient AI models is crucial for sustainable development of AI in the region, as it addresses computational resource constraints and promotes broader accessibility.
A talk will present two projects related to the use of NLP for estimating a client’s depression severity and well-being. The first project examines emotional coherence between the subjective experience of emotions and emotion expression in therapy using transformer-based emotion recognition models. The second project proposes a semantic pipeline to study depression severity in individuals based on their social media posts by exploring different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. Why it matters: This research explores how NLP techniques can be applied to mental health assessment, potentially offering new tools for diagnosis and treatment monitoring.
Zeerak Talat, an independent scholar, gave a talk at MBZUAI on automated content moderation and the impacts of machine learning on society. Talat's research considers how machine learning interacts with and impacts societies through content moderation technologies, drawing from NLP, privacy preserving machine learning, science and technology studies, decolonize studies, and media studies. The talk highlighted research areas that can afford productive directions for the meeting between machine learning and society. Why it matters: The talk contributes to the discussion of ethical AI development and deployment in the region, particularly regarding content moderation and its societal impacts.
The article discusses parameter-efficient fine-tuning methods for large NLP models, highlighting their importance due to the increasing size and computational demands of state-of-the-art language models. It provides an overview of these methods, presenting them in a unified view to emphasize their similarities and differences. Indraneil, a PhD candidate at TU Darmstadt's UKP Lab, is researching parameter-efficient fine-tuning, sparsity, and conditional computation methods to improve LLM performance in multilingual, multi-task settings. Why it matters: Efficient fine-tuning techniques are crucial for democratizing access to and accelerating the deployment of large language models in the region and beyond.
A new content improvement system has been developed to address issues of randomness and incorrectness in text generated by deep learning models like GPT-3. The system uses text mining to identify correct sentences and employs syntactic/semantic generalization to substitute problematic elements. The system can substantially improve the factual correctness and meaningfulness of raw content. Why it matters: Improving the quality of automatically generated content is crucial for ensuring reliability and trustworthiness across various AI applications.
NYU and NYU Abu Dhabi researchers are working on user-centric gender rewriting in NLP, especially for Arabic. They are building an Arabic Parallel Gender Corpus and developing models for gender rewriting tasks. The work aims to address representational harms caused by NLP systems that don't account for user preferences regarding grammatical gender. Why it matters: This research promotes fairness and inclusivity in Arabic NLP by enabling systems to generate gender-specific outputs based on user preferences, mitigating biases present in training data.
Project LITMUS explores predicting cross-lingual transfer accuracy in multilingual language models, even without test data in target languages. The goal is to estimate model performance in low-resource languages and optimize training data for desired cross-lingual performance. This research aims to identify factors influencing cross-lingual transfer, contributing to linguistically fair MMLMs. Why it matters: Improving cross-lingual transfer is vital for creating more equitable and effective multilingual AI systems, especially for languages with limited resources.
Ekaterina Vylomova from the University of Melbourne gave a talk on using NLP models to advance research in linguistic morphology, typology, and social psychology. The talk covered using models to study morphology, phonetic changes in words over time, and diachronic changes in language semantics. Vylomova presented the UniMorph project, a cross-lingual annotation schema and database with morphological paradigms for over 150 languages. Why it matters: This research demonstrates the potential of NLP to contribute to a deeper understanding of language evolution and structure, with applications in linguistic research and the study of social and cultural changes.