This study evaluated the adversarial robustness of five state-of-the-art Arabic Language Models against various Arabic adversarial attacks at character, word, and sentence levels. It found that diacritic insertion could reduce model accuracy by up to 92%, while manipulating Arabic conjunctions led to a 58% accuracy degradation, and paraphrasing reduced performance by an average of 76%. While adversarial training improved overall resilience, particularly for MARBERT and AraBERT, challenges against character-level noise persist. Why it matters: These findings are crucial for understanding and mitigating security vulnerabilities in Arabic AI, guiding the development of more robust and safe Arabic NLP systems.
The paper introduces ALPS (Arabic Linguistic & Pragmatic Suite), a diagnostic challenge set for evaluating deep semantics and pragmatics in Arabic NLP. The dataset contains 531 expert-curated questions across 15 tasks and 47 subtasks, designed to test morpho-syntactic dependencies and compositional semantics. Evaluation of 23 models, including commercial, open-source, and Arabic-native models, reveals that models struggle with fundamental morpho-syntactic dependencies, especially those reliant on diacritics. Why it matters: ALPS provides a valuable benchmark for evaluating the linguistic competence of Arabic NLP models, highlighting areas where current models fall short despite achieving high fluency.
Francisco Rodríguez-Henríquez, Technical Director at TII's Cryptography Research Center, has been appointed to the Board of Directors of the International Association for Cryptologic Research (IACR). He is the first cryptographer from the Middle East to join the IACR board. His three-year tenure aims to foster cryptography development in the Middle East through conferences and seminars, and promote openly accessible cryptographic software and hardware. Why it matters: This appointment signals growing recognition of Middle Eastern expertise in cryptography and enhances the region's role in shaping global cryptographic research and development.
The Cryptography Research Center (CRC) of the Technology Innovation Institute (TII) in Abu Dhabi is co-hosting the 21st Cryptology and Network Security Conference (CANS 2022) in cooperation with Springer, IACR, and NYU Abu Dhabi. The conference includes a workshop on AI and automation in cryptography and network security, with talks from experts like Claudia Diaz from Nym Technologies. Topics covered include post-quantum cryptography, blockchain security, and cryptanalysis techniques. Why it matters: This event highlights the UAE's growing role as a hub for cutting-edge research in cryptography and cybersecurity, fostering collaboration between academia and industry.
Cryptography Research Center's Prof. Francisco Rodriguez-Henriquez and PhD candidate Jorge Chavez-Saab won a Best Paper Award ahead of Asiacrypt 2022. Their paper, "SwiftEC: Shallue-van de Woestijne Indifferentiable Function to Elliptic Curves," was written in collaboration with Mehdi Tibouchi of NTT. The paper presents an improved variation of the Elligator Squared technique for representing points of arbitrary elliptic curves as close-to-uniform random strings. Why it matters: The award recognizes important cryptographic research from the UAE, contributing to the advancement of secure digital solutions.
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.
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.