Natural language processing research from GCC institutions, covering Arabic NLP, multilingual models, text classification, named entity recognition, and machine translation.
The UAE is reportedly leveraging Artificial Intelligence to address and bridge existing language gaps within its legal system. This initiative aims to enhance the clarity, accuracy, and efficiency of legal processes, particularly in a multi-lingual environment. Utilizing AI tools is expected to streamline the interpretation and understanding of legal documents and proceedings across diverse linguistic contexts. Why it matters: This development underscores the UAE's strategic commitment to integrating advanced technologies for improving governmental services and operational effectiveness.
Nanovate, an Arabic AI startup, has secured $1 million in pre-seed funding. The round was led by Hala Ventures, with participation from angel investors. Nanovate plans to use the funds to scale its operations across the GCC region. Why it matters: This investment highlights the growing interest in Arabic-focused AI solutions and the potential for startups to address specific regional needs.
The ArabJobs dataset is a new corpus of over 8,500 Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the UAE. The dataset contains over 550,000 words and captures linguistic, regional, and socio-economic variation in the Arab labor market. It is available on GitHub and can be used for fairness-aware Arabic NLP and labor market research.
This study explores fine-tuning large language models (LLMs) for Arabic medical text generation to improve hospital management systems. A unique dataset was collected from social media, capturing medical conversations between patients and doctors, and used to fine-tune models like Mistral-7B, LLaMA-2-7B, and GPT-2. The fine-tuned Mistral-7B model outperformed the others with a BERT F1-score of 68.5%. Why it matters: The research demonstrates the potential of generative AI to provide scalable and culturally relevant solutions for healthcare challenges in Arabic-speaking regions.
Egyptian AI startup Intella, specializing in Arabic speech recognition, has raised $12.5 million in funding. The round was led by বিনিয়োগ, with participation from other investors. Intella plans to use the capital to expand its Arabic AI speech models and related services. Why it matters: The funding will help advance Arabic language AI capabilities, which are currently underserved compared to English-centric models.
The paper introduces a benchmark of 1,000 multiple-choice questions to evaluate LLMs on Islamic inheritance law ('ilm al-mawarith). Seven LLMs were tested, with o3 and Gemini 2.5 achieving over 90% accuracy, while ALLaM, Fanar, LLaMA, and Mistral scored below 50%. Error analysis revealed limitations in handling structured legal reasoning. Why it matters: This research highlights the challenges and opportunities for adapting LLMs to complex, culturally-specific legal domains like Islamic jurisprudence.
This paper introduces an enhanced Dense Passage Retrieval (DPR) framework tailored for Arabic text retrieval. The core innovation is an Attentive Relevance Scoring (ARS) mechanism that improves semantic relevance modeling between questions and passages, replacing standard interaction methods. The method integrates pre-trained Arabic language models and architectural refinements, achieving improved retrieval and ranking accuracy for Arabic question answering. Why it matters: This work addresses the underrepresentation of Arabic in NLP research by providing a novel approach and publicly available code to improve Arabic text retrieval, which can benefit various applications like Arabic search engines and question-answering systems.
This paper benchmarks multilingual and monolingual LLM performance across Arabic, English, and Indic languages, examining model compression effects like pruning and quantization. Multilingual models outperform language-specific counterparts, demonstrating cross-lingual transfer. Quantization maintains accuracy while promoting efficiency, but aggressive pruning compromises performance, particularly in larger models. Why it matters: The findings highlight strategies for scalable and fair multilingual NLP, addressing hallucination and generalization errors in low-resource languages.
This paper introduces an AI-driven decision support system for green hydrogen investment in Oman, specifically for the Duqm R3 auction. The system uses publicly available meteorological data to predict maintenance pressure on hydrogen infrastructure, creating a Maintenance Pressure Index (MPI). This tool supports regulatory oversight and operational decision-making by enabling temporal benchmarking against performance claims.
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.
This article surveys the landscape of Arabic Large Language Models (ALLMs), tracing their evolution from early text processing systems to sophisticated AI models. It highlights the unique challenges and opportunities in developing ALLMs for the 422 million Arabic speakers across 27 countries. The paper also examines the evaluation of ALLMs through benchmarks and public leaderboards. Why it matters: ALLMs can bridge technological gaps and empower Arabic-speaking communities by catering to their specific linguistic and cultural needs.
This paper critically examines common assumptions about Arabic dialects used in NLP. The authors analyze a multi-label dataset where sentences in 11 country-level dialects were assessed by native speakers. The analysis reveals that widely held assumptions about dialect grouping and distinctions are oversimplified and not always accurate. Why it matters: The findings suggest that current approaches in Arabic NLP tasks like dialect identification may be limited by these inaccurate assumptions, hindering further progress in the field.
KAUST researchers introduced MOLE, a framework leveraging LLMs for automated metadata extraction from scientific papers. The system processes documents in multiple formats and validates outputs, targeting datasets beyond Arabic. A new benchmark dataset has been released to evaluate progress in metadata extraction.
This study reviews the use of large language models (LLMs) for Arabic language processing, focusing on pre-trained models and their applications. It highlights the challenges in Arabic NLP due to the language's complexity and the relative scarcity of resources. The review also discusses how techniques like fine-tuning and prompt engineering enhance model performance on Arabic benchmarks. Why it matters: This overview helps consolidate research directions and benchmarks in Arabic NLP, guiding future development of LLMs tailored for the Arabic language and its diverse dialects.
A new dataset for Arabic proper noun diacritization was introduced, addressing the ambiguity caused by undiacritized proper nouns in Arabic Wikipedia. The dataset includes manually diacritized Arabic proper nouns of various origins along with their English Wikipedia glosses. GPT-4o was benchmarked on the task of recovering full diacritization from undiacritized Arabic and English forms, achieving 73% accuracy. Why it matters: The release of this dataset should facilitate further research on Arabic Wikipedia proper noun diacritization, improving the accessibility and accuracy of Arabic NLP resources.
The paper introduces Sadeed, a fine-tuned decoder-only language model based on the Kuwain 1.5B Hennara model, for improved Arabic text diacritization. Sadeed is fine-tuned on high-quality diacritized datasets and achieves competitive results compared to larger proprietary models. The authors also introduce SadeedDiac-25, a new benchmark for fairer evaluation of Arabic diacritization across diverse text genres. Why it matters: This work advances Arabic NLP by providing both a competitive diacritization model and a more robust evaluation benchmark, facilitating further research and development in the field.
KAUST held its Rising Stars in AI Symposium 2025, organized by the Center of Excellence for Generative AI, from April 7-10. The symposium hosted 25 emerging researchers to present their work in generative AI, machine learning, CV, and NLP. KAUST leadership emphasized the university's commitment to AI research and its role in fostering global collaboration and innovation in the field. Why it matters: The event highlights KAUST's ambition to become a central hub for AI research and talent development in Saudi Arabia, aligning with the Kingdom's broader AI strategy.
This paper explores the use of AI and social media analytics to detect sustainability trends in Saudi Arabia's evolving market, in line with Vision 2030. The study processes millions of social media posts, news articles, and blogs to understand sustainability trends across various sectors. The AI-driven methodology offers sector-specific and cross-sector insights, providing decision-makers with a snapshot of market shifts, and can be adapted to other regions.
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.
The Qatar Computing Research Institute (QCRI) hosted the MenaML 2024 Winter School, focusing on machine learning. The school featured lectures, tutorials, and hands-on sessions covering various ML topics. Participants included students and researchers from the MENA region. Why it matters: This event fosters regional expertise and collaboration in AI and machine learning.
This paper explores how AI and social media analytics can identify and track trends in Saudi Arabia across sectors such as construction, food and beverage, tourism, technology, and entertainment. The study analyzed millions of social media posts each month, classifying discussions and calculating scores to track trends. The AI-driven methodology was able to predict the emergence and growth of trends by utilizing social media data.
A study investigated language shift from Tibetan to Arabic among Tibetan families who migrated to Saudi Arabia 70 years ago. Data from 96 participants across three age groups revealed significant intergenerational differences in language use. Younger members rarely used Tibetan, while older members used it slightly more, with a p-value of .001 indicating statistical significance.
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 paper introduces MIRAGE, a framework for evaluating LLMs' ability to simulate human behaviors in murder mystery games. MIRAGE uses four methods: TII, CIC, ICI and SCI to assess the LLMs' role-playing proficiency. Experiments show that even GPT-4 struggles with the complexities of the MIRAGE framework.
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.
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.
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.
OSHRM (Oman Society for Human Resources Management) has partnered with Elevatus, an AI-powered recruitment platform. The collaboration aims to accelerate Oman's digital transformation efforts, specifically within the human resources sector. This initiative will likely integrate advanced technological solutions to enhance talent acquisition and HR processes across Omani organizations. Why it matters: This partnership contributes to Oman's national digital strategy by leveraging AI to modernize critical HR functions and improve overall operational efficiency.
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.
MBZUAI researchers release LLM-DetectAIve, a tool for fine-grained detection of machine-generated text across four categories: human-written, machine-generated, machine-written then humanized, and human-written then machine-polished. The tool aims to address concerns about misuse of LLMs, especially in education and academia, by identifying attempts to obfuscate or polish content. LLM-DetectAIve is publicly accessible with code and a demonstration video provided.
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.
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.
24 startups from the seventh TAQADAM cohort at KAUST are preparing to pitch their business ideas to investors on March 3, 2024, after surviving the accelerator program. The startups received mentorship in product design, pricing, and legal matters. Example startups include Cognna (cybersecurity), BuildBench (construction platform), Talents Arena (AI recruitment), and Trh Capital (Shariah-compliant finance). Why it matters: This KAUST program highlights growing entrepreneurial activity and venture capital interest in Saudi startups, especially in areas like cybersecurity and AI.
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 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 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.
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.
KAUST researchers led by Professor Pei-Ying Hong reported new insights into bacterial transformation, potentially impacting wastewater treatment policies. Professor Havard Rue's group released a new statistical package for modeling non-Gaussian datasets, compatible with commercial software. These achievements highlight KAUST's contributions to environmental science and statistical computing. Why it matters: These research outputs strengthen KAUST's reputation as a leading research institution in Saudi Arabia, with practical implications for environmental policy and advanced data analysis.
Researchers introduce TII-SSRC-23, a new network intrusion detection dataset designed to improve the diversity and representation of modern network traffic for machine learning models. The dataset includes a range of traffic types and subtypes to address the limitations of existing datasets. Feature importance analysis and baseline experiments for supervised and unsupervised intrusion detection are also provided.
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.
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.