This paper surveys the landscape of code-switched Arabic natural language processing, covering the mixture of Modern Standard Arabic, dialects, and foreign languages. It examines current efforts, challenges, and research gaps in the field. The survey also provides recommendations for future research directions in code-switched Arabic NLP. Why it matters: Understanding code-switching is crucial for developing effective language technologies that can handle the diverse linguistic landscape of the Arab world.
The GenAI Content Detection Task 1 is a shared task on detecting machine-generated text, featuring monolingual (English) and multilingual subtasks. The task, part of the GenAI workshop at COLING 2025, attracted 36 teams for the English subtask and 26 for the multilingual one. The organizers provide a detailed overview of the data, results, system rankings, and analysis of the submitted systems.
This paper introduces Arabic language integration into Vision-and-Language Navigation (VLN) in robotics, evaluating multilingual SLMs like GPT-4o mini, Llama 3 8B, Phi-3 14B, and Jais using the NavGPT framework. The study uses the R2R dataset to assess the impact of language on navigation reasoning through zero-shot sequential action prediction. Results show the framework enables high-level planning in both English and Arabic, though some models face challenges with Arabic due to reasoning limitations and parsing issues. Why it matters: This work highlights the need to improve language model planning and reasoning for effective navigation, especially to unlock the potential of Arabic-language models in real-world applications.
The article outlines the current landscape of Artificial Intelligence regulations within the Gulf Cooperation Council (GCC) countries. It discusses the differing approaches and frameworks being developed by nations like the UAE and Saudi Arabia to govern AI technologies. The analysis covers aspects such as data privacy, ethical considerations, and the legal implications of AI deployment across the region. Why it matters: Understanding these evolving regulations is crucial for businesses and researchers operating in the GCC to ensure compliance and foster responsible AI innovation and adoption.
Researchers fine-tuned the Qwen2-1.5B model for Arabic using QLoRA on a 4GB VRAM system, using datasets like Bactrian and Arabic Wikipedia. They addressed challenges in Arabic NLP including morphology and dialectal variations. After 10,000 training steps, the final loss converged to 0.1083 with improved handling of Arabic-specific linguistic phenomena. Why it matters: This demonstrates a resource-efficient approach for creating specialized Arabic language models, democratizing access to advanced NLP technologies.
The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in Abu Dhabi as part of COLING 2025. It provided a forum for researchers to share work on language models for low-resource languages. The workshop accepted 35 papers from 52 submissions, covering diverse languages and research areas.
The Library of Congress has published Part Two of its FALQs series, providing an analysis of AI regulations across the Gulf Cooperation Council (GCC) member states. This installment details the various regulatory frameworks, data governance policies, and ethical guidelines being developed and implemented in countries such as the UAE, Saudi Arabia, and Qatar. It offers an overview of how GCC nations are approaching the legal and ethical challenges associated with artificial intelligence. Why it matters: This comprehensive overview is crucial for understanding the evolving landscape of AI governance in the Middle East and its implications for technological development and adoption in the region.
A novel wind speed forecasting (WSF) framework is proposed combining Wavelet Packet Decomposition (WPD), Seasonal Adjustment Method (SAM), and Bidirectional Long Short-term Memory (BiLSTM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The model was tested on five years of hourly wind speed observations acquired from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, achieving high forecasting accuracy.
Researchers introduce CESAR, a convolutional echo state autoencoder for high-resolution wind forecasting. The model extracts spatial features using a deep convolutional autoencoder and models their dynamics with an echo state network. Tested on high-resolution simulations in Riyadh, Saudi Arabia, CESAR improved wind speed and power forecasting by up to 17% compared to other methods. Why it matters: Accurate wind forecasting is critical for efficient wind farm planning and management in Saudi Arabia and the broader region.
Qatar Foundation's Qatar Science & Technology Park (QSTP) recently hosted its inaugural AI Week, attracting over 3,000 local and global tech experts, academics, and entrepreneurs. The event facilitated discussions on the latest advancements, challenges, and opportunities in artificial intelligence, including ethical considerations and the development of local talent. Participants engaged in various sessions, workshops, and networking events aimed at fostering innovation within Qatar's AI ecosystem. Why it matters: This event signifies Qatar's growing commitment to becoming a hub for AI development and research, promoting collaboration and knowledge exchange within the region's tech landscape.
KAUST and the King Salman Center for Disability Research (KSCDR) have signed an MoU to collaborate on the diagnosis, management, and treatment of disabilities affecting Saudi citizens and residents. The partnership will focus on neurodevelopmental conditions, learning disabilities, visual impairments, speech disorders, and mobility impairments. KAUST's Center of Excellence for Smart Health, launched on July 1, will be a key component, leveraging its supercomputing resources and genome sequencing capabilities. Why it matters: This partnership aims to address the increasing prevalence of chronic diseases and disabilities in Saudi Arabia, aligning with national research priorities and improving the quality of life for people with disabilities.
This paper explores multilingual satire detection methods in English and Arabic using zero-shot and chain-of-thought (CoT) prompting. It compares the performance of Jais-chat(13B) and LLaMA-2-chat(7B) on distinguishing satire from truthful news. Results show that CoT prompting significantly improves Jais-chat's performance, achieving an F1-score of 80% in English. Why it matters: This demonstrates the potential of Arabic LLMs like Jais to handle nuanced language tasks such as satire detection, which is critical for combating misinformation in the region.
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
Researchers introduce a new task for generating question-passage pairs to aid in developing regulatory question-answering (QA) systems. The ObliQA dataset, comprising 27,869 questions from Abu Dhabi Global Markets (ADGM) financial regulations, is presented. A baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system is designed and evaluated using the RePASs metric.
Saudi Arabia is implementing various educational reforms and initiatives to equip its student population with the necessary skills for future high-tech AI jobs. These efforts include integrating AI curricula into university programs, establishing specialized academies and training centers, and fostering collaborations between academic institutions and industry leaders. The Kingdom aims to cultivate a robust local talent pool capable of driving its digital transformation and economic diversification goals under Vision 2030. Why it matters: These strategic investments in AI education are crucial for Saudi Arabia to reduce its reliance on oil, build a knowledge-based economy, and position itself as a regional leader in emerging technologies.
The AraFinNLP 2024 shared task introduced two subtasks focused on Arabic financial NLP: multi-dialect intent detection and cross-dialect translation with intent preservation. It utilized the updated ArBanking77 dataset, containing 39k parallel queries in MSA and four dialects, labeled with 77 banking-related intents. 45 teams registered, with 11 participating in intent detection (achieving a top F1 score of 0.8773) and only 1 team attempting translation (achieving a BLEU score of 1.667). Why it matters: This initiative addresses the need for specialized Arabic NLP tools in the growing Arab financial sector, promoting advancements in areas like banking chatbots and machine translation.
Google is funding several KAUST research projects with seed grants totaling $100,000. The projects focus on advancing multilingual, multimodal machine learning, particularly in generative and large language models (LLMs). KAUST faculty will conduct research in areas such as health, cross-cultural language understanding, sustainability, privacy, and education. Why it matters: This collaboration signifies growing investment in AI research within Saudi Arabia, fostering innovation and talent development at a leading institution like KAUST.
The Arab Reform Initiative published a paper summarizing the national AI strategies of countries in the Arab region. The paper identifies common themes such as economic diversification, government efficiency, and education reform. It also notes the varying levels of investment and implementation across different countries. Why it matters: The report provides a useful overview of AI policy in the region, highlighting both opportunities and challenges for responsible AI development.
The fifth Nuanced Arabic Dialect Identification (NADI) 2024 shared task aimed to advance Arabic NLP through dialect identification and dialect-to-MSA machine translation. 51 teams registered, with 12 participating and submitting 76 valid submissions across three subtasks. The winning teams achieved 50.57 F1 for multi-label dialect identification, 0.1403 RMSE for dialectness level identification, and 20.44 BLEU for dialect-to-MSA translation. Why it matters: The results highlight the continued challenges in Arabic dialect processing and provide a benchmark for future research in this area.
The paper addresses the challenge of missing diacritics in Arabic NLP by exploring naturally occurring diacritics in a new dataset across six genres. It maps partially diacritized words to their full diacritization and proposes extensions to the analyze-and-disambiguate approach. The extended diacritization algorithm achieves notable improvements, and the code/datasets are released as open source. Why it matters: This research provides valuable resources and methods for improving Arabic text processing, especially in contexts where diacritization is crucial for accurate interpretation.
KAUST, in collaboration with KSU and KFUPM, is working on a project initiated by the Saudi Communications, Space & Technology Commission (CST) to expand mobile communication coverage in remote areas of the Kingdom. The study explores utilizing the sub-700 MHz ultrahigh frequency (UHF) band, potentially reassigning it from television broadcast to mobile telecommunication networks. This band's long wavelength radio waves can travel further and penetrate obstacles more easily, reducing network infrastructure costs. Why it matters: This initiative could bridge the digital divide in Saudi Arabia by providing affordable mobile connectivity to underserved communities.
This paper introduces a method using Stable Diffusion XL (SDXL) fine-tuned with LoRA to generate culturally relevant coloring templates based on Emirati Al-Sadu weaving patterns for mental health therapy. The approach aims to leverage coloring therapy's stress-relieving benefits while embedding cultural resonance, potentially aiding in the treatment of Generalized Anxiety Disorder (GAD). Future research will explore the impact of Emirati heritage art on Emirati individuals using biosignals to assess engagement and effectiveness.
The National Bank of Kuwait (NBK) and Zain have signed a Memorandum of Understanding (MoU) with the Kuwait Foundation for the Advancement of Sciences (KFAS). This agreement establishes a digital program aimed at developing the skills and shaping the future of Kuwaiti youth. The initiative seeks to empower the younger generation with essential digital competencies for the evolving job market. Why it matters: Such collaborations are vital for fostering local digital talent and preparing the next generation for an AI-driven economy in Kuwait and the broader GCC region.
The study introduces AraSpider, the first Arabic version of the Spider dataset, to advance Arabic NLP. Four multilingual translation models and two text-to-SQL models (ChatGPT 3.5 and SQLCoder) were evaluated. Back translation significantly improved the performance of both ChatGPT 3.5 and SQLCoder on the AraSpider dataset. Why it matters: This work democratizes access to text-to-SQL resources for Arabic speakers and provides a methodology for translating datasets to other languages.
The paper introduces a framework for camel farm monitoring using a combination of automated annotation and fine-tune distillation. The Unified Auto-Annotation framework uses GroundingDINO and SAM to automatically annotate surveillance video data. The Fine-Tune Distillation framework then fine-tunes student models like YOLOv8, transferring knowledge from a larger teacher model, using data from Al-Marmoom Camel Farm in Dubai.
This paper introduces a novel fuzzy clustering method for circular time series based on a new dependence measure that considers circular arcs. The algorithm groups series generated from similar stochastic processes and demonstrates computational efficiency. The method is applied to time series of wind direction in Saudi Arabia, showcasing its practical potential.
The Qatar Computing Research Institute (QCRI) recently concluded a Generative AI Hackathon, bringing together participants to innovate with generative artificial intelligence technologies. The event aimed to foster local talent and drive advancements in the field within Qatar. Participants developed various solutions and prototypes using generative AI models and tools. Why it matters: This hackathon signifies Qatar's commitment to cultivating a strong AI ecosystem and developing skilled professionals in cutting-edge AI domains like generative AI.
This research explores the use of generative AI, specifically ChatGPT, to create student assessments that align with academic accreditation standards, such as those of the National Center for Academic Accreditation in Saudi Arabia and ABET. The study introduces a method for mapping verbs used in questions to educational outcomes, enabling AI to produce and validate accreditation-compliant questions. A survey of faculty members in Saudi universities showed high acceptance rates for AI-generated exam questions and AI assistance in editing existing questions.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
This paper discusses the integration of AI into education, emphasizing a transdisciplinary approach that connects AI instruction to the broader curriculum and community needs. It delves into the AI program developed for Neom Community School in Saudi Arabia, where AI is taught as a subject and used to learn other subjects through the International Baccalaureate (IB) approach. The proposed method aims to make AI relevant throughout the curriculum by integrating it into Units of Inquiry.
Researchers developed a semantic search tool for the Quran using Arabic NLP techniques. The tool was trained on a dataset of over 30 tafsirs (interpretations) of the Quran. Using the SNxLM model and cosine similarity, the tool identifies Quranic verses most relevant to a user's query, achieving a similarity score of up to 0.97. Why it matters: This tool could significantly improve access to the Quran's teachings for Arabic speakers and researchers, providing a valuable resource for religious study and understanding.
This paper describes the Nexus team's participation in the ArAIEval shared task focused on detecting propaganda and disinformation in Arabic. The team fine-tuned transformer models and experimented with zero- and few-shot learning using GPT-4. Nexus's system achieved 9th place in subtask 1A and 10th place in subtask 2A. Why it matters: The work contributes to the important goal of automatically identifying and mitigating the spread of disinformation in Arabic content, which is critical for maintaining societal trust and informed public discourse.
The fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023) aimed to advance Arabic NLP through shared tasks focused on dialect identification and dialect-to-MSA machine translation. 58 teams registered, with 18 participating across three subtasks: dialect identification, dialect-to-MSA translation, and another translation task. The winning teams achieved 87.27 F1 in dialect identification, 14.76 BLEU in one translation task, and 21.10 BLEU in the other. Why it matters: NADI provides valuable benchmarks and datasets for Arabic dialect processing, encouraging further research in this challenging area.
This study analyzes the evolution of data science vocabulary using 16,018 abstracts containing "data science" over 13 years. It identifies new vocabulary introduction and its integration into scientific literature using techniques like EDA, LSA, LDA, and N-grams. The research compares overall scientific publications with those specific to Saudi Arabia, identifying representative articles based on vocabulary usage. Why it matters: The work provides insights into the development of data science terminology and its specific adoption within the Saudi Arabian research landscape.
The paper introduces the concept of Arabic Level of Dialectness (ALDi), a continuous variable representing the degree of dialectal Arabic in a sentence, arguing that Arabic exists on a spectrum between MSA and DA. They present the AOC-ALDi dataset, comprising 127,835 sentences manually labeled for dialectness level, derived from news articles and user comments. Experiments show a model trained on AOC-ALDi can identify dialectness levels across various corpora and genres. Why it matters: ALDi provides a more nuanced approach to analyzing Arabic text than binary dialect identification, enabling sociolinguistic analysis of stylistic choices.
Qatar Computing Research Institute (QCRI) is undertaking cutting-edge AI research and development, including work on Arabic language technologies, computer vision, and data analytics. QCRI's projects span healthcare, disaster response, and cybersecurity. Why it matters: QCRI's work helps to build local AI expertise and address regional challenges.
An initiative named the 'Elevate Program' has been launched, focusing on empowering women in the fields of data science and Artificial Intelligence. The program aims to enhance the skills and career opportunities for women within these critical technology sectors in the region. It seeks to increase female participation and leadership in the burgeoning AI and data landscape. Why it matters: This program is significant for fostering diversity and inclusion in the Middle East's technology workforce, contributing to human capital development and innovation in AI.
Researchers have introduced LLMeBench, a customizable framework for evaluating large language models (LLMs) across diverse NLP tasks and languages. The framework features generic dataset loaders, multiple model providers, and pre-implemented evaluation metrics, supporting in-context learning with zero- and few-shot settings. LLMeBench was tested on 31 unique NLP tasks using 53 datasets across 90 experimental setups with 296K data points, and the code has been open-sourced. Why it matters: The framework's flexibility and ease of customization should accelerate LLM benchmarking, especially for Arabic and other low-resource languages.
Qatar Computing Research Institute (QCRI) published a paper in the Royal Society Open Science journal focusing on the role of "affective features" in improving Arabic dialect identification. The research demonstrates that incorporating features related to emotions and sentiment enhances the accuracy of identifying different Arabic dialects. The study used a dataset of 14 Levantine dialects. Why it matters: This highlights the growing focus on nuanced Arabic NLP research within the GCC, moving beyond standard language models to incorporate cultural and emotional understanding.
This paper evaluates the performance of GPT-3.5 and GPT-4 on seven Arabic NLP tasks including sentiment analysis, translation, and diacritization. GPT-4 outperforms GPT-3.5 on most tasks. The study provides an analysis of sentiment analysis and introduces a Python interface, Taqyim, for evaluating Arabic NLP tasks. Why it matters: The evaluation of LLMs on Arabic NLP tasks helps to identify strengths and weaknesses, guiding future research and development efforts in the field.
The paper introduces the Unscented Autoencoder (UAE), a novel deep generative model based on the Variational Autoencoder (VAE) framework. The UAE uses the Unscented Transform (UT) for a more informative posterior representation compared to the reparameterization trick in VAEs. It replaces Kullback-Leibler (KL) divergence with the Wasserstein distribution metric and demonstrates competitive performance in Fréchet Inception Distance (FID) scores.
This paper benchmarks the performance of OpenAI's Whisper model on diverse Arabic speech recognition tasks, using publicly available data and novel dialect evaluation sets. The study explores zero-shot, few-shot, and full finetuning scenarios. Results indicate that while Whisper outperforms XLS-R models in zero-shot settings on standard datasets, its performance drops significantly when applied to unseen Arabic dialects.
This paper introduces a novel approach for monitoring and analyzing the evolution of complex geographic objects in satellite image time-series. The method uses a spatiotemporal graph and constraint satisfaction problems (CSP) to model and analyze object changes. Experiments on real-world satellite images from Saudi Arabian cities demonstrate the effectiveness of the proposed approach.
This paper introduces a new task: detecting propaganda techniques in code-switched text. The authors created and released a corpus of 1,030 English-Roman Urdu code-switched texts annotated with 20 propaganda techniques. Experiments show the importance of directly modeling multilinguality and using the right fine-tuning strategy for this task.
KAUST and Frontiers for Young Minds (FYM) have partnered to produce child-friendly science articles in Arabic, engaging young Arabic speakers in scientific research. Their collaboration resulted in the first Arabic-original science article, "The Story of Legionella: From Surviving inside Amoebas to Causing Pneumonia", written by researchers from the University of Louisville. The article was reviewed by children, including two Saudis, with mentorship from a KAUST PhD graduate. Why it matters: This initiative promotes STEAM leadership in the region by making complex scientific concepts accessible to young Arabic-speaking readers, fostering early engagement in science.
A new dataset called the Saudi Privacy Policy Dataset is introduced, which contains Arabic privacy policies from various sectors in Saudi Arabia. The dataset is annotated based on the 10 principles of the Personal Data Protection Law (PDPL) and includes 1,000 websites, 4,638 lines of text, and 775,370 tokens. The dataset aims to facilitate research and development in privacy policy analysis, NLP, and machine learning applications related to data protection.
Researchers in Saudi Arabia are applying computer vision techniques to reduce Camel-Vehicle Collisions (CVCs). They tested object detection models including CenterNet, EfficientDet, Faster R-CNN, SSD, and YOLOv8 on the task, finding YOLOv8 to be the most accurate and efficient. Future work will focus on developing a system to improve road safety in rural areas.
KAUST is launching the "Dear AI" campaign and hackathon to address gender bias and under-representation of women and Saudi/Arab people in AI, after finding AI image tools return only 1% women for prompts like "imagine entrepreneur." The campaign calls for accurate representation in AI datasets from Saudi Arabia and beyond. KAUST notes that 47% of graduates in their AI academy are women. Why it matters: This campaign highlights the need for more inclusive AI training data and addresses gender imbalances in STEM fields in Saudi Arabia.
Researchers are exploring computer vision models to mitigate Camel-Vehicle Collisions (CVC) in Saudi Arabia, which have a high fatality rate. They tested CenterNet, EfficientDet, Faster R-CNN, and SSD for camel detection, finding CenterNet to be the most accurate and efficient. Future work involves developing a comprehensive system to enhance road safety in rural areas.
The Qatar Computing Research Institute (QCRI) provided insights and clarification regarding the impact of artificial intelligence on jobs and employment. This discussion likely centered on the regional economic implications, possibly in the context of the World Cup. The initiative aimed to enhance understanding of the future of work as AI adoption accelerates. Why it matters: Addressing the societal and economic challenges posed by AI's influence on the labor market is critical for GCC nations as they pursue economic diversification and technological advancement.