Under-represented languages suffer from a lack of data, and as a result, there are few LLMs that support them. Extending an existing LLM to a new language is a practical option for startups, university labs, and organizations with limited budgets. This process involves several steps. In this paper, we describe how we adapted the Falcon3-7B model to Arabic, covering everything from data collection and training to evaluation. Falcon-Arabic was trained exclusively on native data to better capture the cultural and linguistic aspects of the language. Our evaluations show that Falcon-Arabic achieves state-of-the-art results on a range of Arabic benchmarks.

ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applications such as salary estimation and job category normalisation using large language models, alongside benchmark tasks for gender bias detection and profession classification. The findings show the utility of ArabJobs for fairness-aware Arabic NLP and labour market research. The dataset is publicly available on GitHub: https://github.com/drelhaj/ArabJobs.

The morphological structure of Semitic languages, such as Arabic, is based on non-concatenative roots and templates. This complex word structure used by humans is obscured to neural models that employ traditional tokenization algorithms, such as byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994). In this work, we present and evaluate Semitic Root Encoding (SRE), a tokenization method that represents both concatenative and non-concatenative structures in Semitic words with sequences of root, template stem, and BPE tokens. We apply the method to neural machine translation (NMT) and find that SRE tokenization yields an average increase of 1.15 BLEU over the baseline. SRE tokenization is also robust against generating combinations of roots with template stems that do not occur in nature. Finally, we compare the performance of SRE to tokenization based on non-linguistic root and template structures and tokenization based on stems, providing evidence that NMT models are capable of leveraging tokens based on non-concatenative Semitic morphology.

Arabic is one of the most widely spoken languages in the world, yet efforts to develop and evaluate Large Language Models (LLMs) for Arabic remain relatively limited. Most existing Arabic benchmarks focus on linguistic, cultural, or religious content, leaving a significant gap in areas like STEM and coding domains that are increasingly relevant for real-world LLM applications. To help bridge this gap, we present 3LM, a suite of three benchmarks designed specifically for Arabic. The first is a set of STEM-related question-answer pairs, naturally sourced from Arabic textbooks and educational worksheets. The second consists of synthetically generated STEM questions, created using the same sources. The third benchmark focuses on code generation, built through a careful translation of two widely used code benchmarks, incorporating a human-in-the-loop process with several rounds of review to ensure high-quality and faithful translations. We release all three benchmarks publicly to support the growth of Arabic LLM research in these essential but underrepresented areas.

We introduce TuniFra, a novel and comprehensive corpus developed to advance research in Automatic Speech Recognition (ASR) and Speech-to-Text Translation (STT) for Tunisian Arabic, a notably low-resourced language variety. The TuniFra corpus comprises 15 hours of native Tunisian Arabic speech, carefully transcribed and manually translated into French. While the development of ASR and STT systems for major languages is supported by extensive datasets, low-resource languages such as Tunisian Arabic face significant challenges due to limited training data, particularly for speech technologies. TuniFra addresses this gap by offering a valuable resource tailored for both ASR and STT tasks in the Tunisian dialect. We describe our methodology for data collection, transcription, and annotation, and present initial baseline results for both Tunisian Arabic speech recognition and Tunisian Arabic–French speech translation.

Cross-lingual retrieval-augmented generation (RAG) is a critical capability for retrieving and generating answers across languages. Prior work in this context has mostly focused on generation and relied on benchmarks derived from open-domain sources, most notably Wikipedia. In such settings, retrieval challenges often remain hidden due to language imbalances, overlap with pretraining data, and memorized content. To address this gap, we study Arabic-English RAG in a domain-specific setting using benchmarks derived from real-world corporate datasets. Our benchmarks include all combinations of languages for the user query and the supporting document, drawn independently and uniformly at random. This enables a systematic study of multilingual retrieval behavior.Our findings reveal that retrieval is a critical bottleneck in cross-lingual domain-specific scenarios, with substantial performance drops occurring when the user query and supporting document languages differ. A key insight is that these failures stem primarily from the retriever’s difficulty in ranking documents across languages. Finally, we propose two simple retrieval strategies that address this source of failure by enforcing equal retrieval from both languages or by translating the query, resulting in substantial improvements in cross-lingual and overall performance. These results highlight meaningful opportunities for improving multilingual retrieval, particularly in practical, real-world RAG applications.

Conversational question-answering (CQA) plays a crucial role in bridging the gap between human language and machine understanding, enabling more natural and interactive interactions with AI systems. In this work, we present the first results on open-domain Arabic CQA using deep learning. We introduce AraQReCC, a large-scale Arabic CQA dataset containing 9K conversations with 62K question-answer pairs, created by translating a subset of the QReCC dataset. To ensure data quality, we used COMET-based filtering and manual ratings from large language models (LLMs), such as GPT-4 and LLaMA, selecting conversations with COMET scores, along with LLM ratings of 4 or more. AraQReCC facilitates advanced research in Arabic CQA, improving clarity and relevance through question rewriting. We applied AraT5 for question rewriting and used BM25 and Dense Passage Retrieval (DPR) for passage retrieval. AraT5 is also used for question answering, completing the end-to-end system. Our experiments show that the best performance is achieved with DPR, attaining an F1 score of 21.51% on the test set. While this falls short of the human upper bound of 40.22%, it underscores the importance of question rewriting and quality-controlled data in enhancing system performance.

Classical Arabic represents a significant era that encompasses the golden age of Arab culture, philosophy, and scientific literature. With a broad consensus on the importance of translating these literatures to enrich knowledge dissemination across communities, the advent of large language models (LLMs) and translation systems offers promising tools to facilitate this goal. However, we have identified a scarcity of translation datasets in Classical Arabic, which are often limited in scope and topics, hindering the development of high-quality translation systems. In response, we present the ATHAR dataset, which comprises 66,000 high-quality classical Arabic to English translation samples that cover a wide array of topics including science, culture, and philosophy. Furthermore, we assess the performance of current state-of-the-art LLMs under various settings, concluding that there is a need for such datasets in current systems. Our findings highlight how models can benefit from fine-tuning or incorporating this dataset into their pretraining pipelines. The dataset is publicly available on the HuggingFace Data Hub. To preserve anonymity during review, we additionally provide an anonymized snapshot at https://drive.google.com/drive/folders/1c_ElsblaOJzQ0TW_M1DugjR2o3Xv9RUo.

We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question generator, an evaluator, and a swarm of answer generators, our system iteratively refines its own performance without any human intervention. Starting from raw, multi-page Arabic documents across diverse domains, the question generator produces fine-grained, context-aware queries to be tackled by the answer generator swarm, and the evaluator assesses and feeds back quality metrics. This closed-loop cycle enables continuous learning: low-confidence outputs trigger automated re-generation and model updates, progressively enhancing question difficulty and relevance. Moreover, we set the quality metrics as a tunable hyperparameter, enabling question generation at controllable and customizable difficulty levels. We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges spanning hundreds of pages, and demonstrate that our self-evolving workflow substantially outperform static pipelines, markedly boosting the long-context comprehension capabilities of leading Arabic Large Vision Language Models (LVLMs). Lastly, we also meticulously architect a fully automated agentic workflow for long-context Arabic document collection.

Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios.

For effective use in specific countries, Large Language Models (LLMs) need a strong grasp of local culture and core knowledge to ensure socially appropriate, context-aware, and factually correct responses. Existing Arabic and Saudi benchmarks are limited, focusing mainly on dialects or lifestyle, with little attention to deeper cultural or domain-specific alignment from authoritative sources. To address this gap and the challenge LLMs face with non-Western cultural nuance, this study introduces the Saudi-Alignment Benchmark. It consists of 874 manually curated questions across two core cultural dimensions: Saudi Cultural and Ethical Norms, and Saudi Domain Knowledge. These questions span multiple subcategories and use three formats to assess different goals with verified sources. Our evaluation reveals significant variance in LLM alignment. GPT-4 achieved the highest overall accuracy (83.3%), followed by ALLaM-7B (81.8%) and Llama-3.3-70B (81.6%), whereas Jais-30B exhibited a pronounced shortfall at 21.9%. Furthermore, multilingual LLMs excelled in norms; ALLaM-7B in domain knowledge. Considering the effect of question format, LLMs generally excelled in selected-response formats but showed weaker results on generative tasks, indicating that recognition-based benchmarks alone may overestimate cultural and contextual alignment. These findings highlight the need for tailored benchmarks and reveal LLMs’ limitations in achieving cultural grounding, particularly in underrepresented contexts like Saudi Arabia.

Recently, extensive research on the hallucination of the large language models (LLMs) has mainly focused on the English language. Despite the growing number of multilingual and Arabic-specific LLMs, evaluating LLMs’ hallucination in the Arabic context remains relatively underexplored. The knowledge gap is particularly pressing given Arabic’s widespread use across many regions and its importance in global communication and media. This paper presents the first comprehensive hallucination evaluation of Arabic and multilingual LLMs on two critical Arabic natural language generation tasks: generative question answering (GQA) and summarization. This study evaluates a total of 12 LLMs, including 4 Arabic pre-trained models, 4 multilingual models, and 4 reasoning-based models. To assess the factual consistency and faithfulness of LLMs’ outputs, we developed a fine-grained hallucination evaluation framework consisting of 12 fine-grained hallucination indicators that represent the varying characteristics of each task. The results reveal that factual hallucinations are more prevalent than faithfulness errors across all models and tasks. Notably, the Arabic pre-trained model Allam consistently demonstrates lower hallucination rates than multilingual models and a comparative performance with reasoning-based models. The code is available at: https://github.com/aishaalansari57/AraHalluEval

Prompt relevance is a critical yet underexplored dimension in Arabic Automated Essay Scoring (AES). We present the first systematic study of binary prompt-essay relevance classification, supporting both AES scoring and dataset annotation. To address data scarcity, we built a synthetic dataset of on-topic and off-topic pairs and evaluated multiple models, including threshold-based classifiers, SVMs, causal LLMs, and a fine-tuned masked SBERT model. For real-data evaluation, we combined QAES with ZAEBUC, creating off-topic pairs via mismatched prompts. We also tested prompt expansion strategies using AraVec, CAMeL, and GPT-4o. Our fine-tuned SBERT achieved 98% F1 on synthetic data and strong results on QAES+ZAEBUC, outperforming SVMs and threshold-based baselines and offering a resource-efficient alternative to LLMs. This work establishes the first benchmark for Arabic prompt relevance and provides practical strategies for low-resource AES.

Information Extraction tasks such as Named Entity Recognition and Relation Extraction are often developed using diverse tagsets and annotation guidelines. This presents major challenges for model generalization, cross-dataset evaluation, tool interoperability, and broader industry adoption. To address these issues, we propose an information extraction ontology, , which covers a wide range of named entity types and relations. serves as a semantic mediation framework that facilitates alignment across heterogeneous tagsets and annotation guidelines. We propose two ontology-based mapping methods: (i) as a set of mapping rules for uni-directional tagset alignment; and (ii) as ontology-based prompting, which incorporates the ontology concepts directly into prompts, enabling large language models (LLMs) to perform more effective and bi-directional mappings. Our experiments show a 15% improvement in out-of-domain mapping accuracy when using ontology-based prompting compared to rule-based methods. Furthermore, is aligned with Schema.org and Wikidata, enabling interoperability with knowledge graphs and facilitating broader industry adoption. The is open source and available at https://sina.birzeit.edu/wojood.

This paper presents a methodology for inserting phrases in Arabic poems to conform to a specific rhythm using ByT5, a byte-level multilingual transformer-based model. Our work discusses a rule-based grapheme-to-beat transformation tailored for extracting the rhythm from fully diacritized Arabic script. Our approach employs a conditional denoising objective to fine-tune ByT5, where the model reconstructs masked words to match a target rhythm. We adopt a curriculum learning strategy, pre-training on a general Arabic dataset before fine-tuning on poetic dataset, and explore cross-lingual transfer from English to Arabic. Experimental results demonstrate that our models achieve high rhythmic alignment while maintaining semantic coherence. The proposed model has the potential to be used in co-creative applications in the process of composing classical Arabic poems.

The Holy Qur’an provides timeless guidance, addressing modern challenges and offering answers to many important questions. The Qur’an QA 2023 shared task introduced the Qur’anic Passage Retrieval (QPR) task, which involves retrieving relevant passages in response to MSA questions. In this work, we evaluate the ability of seven pre-trained large language models (LLMs) to retrieve relevant passages from the Qur’an in response to given questions, considering zero-shot and several few-shot scenarios. Our experiments show that the best model, Claude, significantly outperforms the state-of-the-art QPR model by 28 points on MAP and 38 points on MRR, exhibiting an impressive improvement of about 113% and 82%, respectively.

Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance. Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutions, preserving critical emotional cues often lost in traditional methods. While recent models favor large-scale architectures with millions of parameters, ArabEmoNet achieves superior results with just 1 million parameters—90 times smaller than HuBERT base and 74 times smaller than Whisper. This efficiency makes it ideal for resource-constrained environments. ArabEmoNet advances Arabic speech emotion recognition, offering exceptional performance and accessibility for real-world applications.

We present the first publicly available, multidimensional corpus of Qatari Arabic that captures intra-dialectal variation across Urban and Bedouin speakers. While often grouped under the label of “Gulf Arabic”, Qatari Arabic exhibits rich phonological, lexical, and discourse-level differences shaped by gender, age, and sociocultural identity. Our dataset includes aligned speech and transcriptions from 255 speakers, stratified by gender and age, and collected through structured interviews on culturally salient topics such as education, heritage, and social norms. The corpus reveals systematic variation in pronunciation, vocabulary, and narrative style, offering insights for both sociolinguistic analysis and computational modeling. We also demonstrate its utility through preliminary experiments in the prediction of dialects and genders. This work provides the first large-scale, demographically balanced corpus of Qatari Arabic, laying a foundation for both sociolinguistic research and the development of dialect-aware NLP systems.

Automated Essay Scoring (AES) has shown significant advancements in educational assessment. However, under-resourced languages like Arabic have received limited attention. To bridge this gap and enable robust Arabic AES, this paper introduces the first publicly-available comprehensive set of engineered features tailored for Arabic AES, covering surface-level, readability, lexical, syntactic, and semantic features. Experiments are conducted on a dataset of 620 Arabic essays, each annotated with both holistic and trait-specific scores. Our findings demonstrate that the proposed feature set is effective across different models and competitive with recent NLP advances including LLMs, establishing the state-of-the-art performance and providing strong baselines for future Arabic AES research. Moroever, the resulting feature set offers a reusable and foundational resource, contributing towards the development of more effective Arabic AES systems.

This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, ʿilm al-mawārīth. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering diverse inheritance scenarios, designed to test each model’s ability—from understanding the inheritance context to computing the distribution of shares prescribed by Islamic jurisprudence. The results show a wide performance gap among models. o3 and Gemini 2.5 achieved accuracies above 90%, while ALLaM, Fanar, LLaMA, and Mistral scored below 50%. These disparities reflect important differences in reasoning ability and domain adaptation.We conduct a detailed error analysis to identify recurring failure patterns across models, including misunderstandings of inheritance scenarios, incorrect application of legal rules, and insufficient domain knowledge. Our findings highlight the limitations of current models in handling structured legal reasoning and suggest directions for improving their performance in Islamic legal reasoning.

The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it challenging to measure actual progress and to mitigate data contamination. Here, we aim to bridge these gaps. In particular, we introduce BALSAM, a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation. It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation. We envision BALSAM as a unifying platform that sets standards and promotes collaborative research to advance Arabic LLM capabilities.

In this paper, we introduce TEDxTN, the first publicly available Tunisian Arabic to English speech translation dataset. This work is in line with the ongoing effort to mitigate the data scarcity obstacle for a number of Arabic dialects. We collected, segmented, transcribed and translated 108 TEDx talks following our internally developed annotations guidelines. The. collected talks represent 25 hours of speech with code-switching that cover speakers with various accents from over 11 different regions of Tunisia. We make the annotation guidelines and corpus publicly available. This will enable the extension of TEDxTN to new talks as they become available. We also report results for strong baseline systems of Speech Recognition and Speech Translation using multiple pre-trained and fine-tuned end-to-end models. This corpus is the first open source and publicly available speech translation corpus of Code-Switching Tunisian dialect. We believe that this is a valuable resource that can motivate and facilitate further research studying Tunisian Dialect.

Video-to-text and text-to-video retrieval are dominated by English benchmarks (e.g. DiDeMo, MSR-VTT) and recent multilingual corpora (e.g. RUDDER), yet Arabic remains underserved, lacking localized evaluation metrics. We introduce a three-stage framework, AutoArabic, utilizing state-of-the-art large language models (LLMs) to translate non-Arabic benchmarks into Modern Standard Arabic, reducing the manual revision required by nearly fourfold. The framework incorporates an error detection module that automatically flags potential translation errors with 97% accuracy. Applying the framework to DiDeMo, a video retrieval benchmark produces DiDeMo-AR, an Arabic variant with 40,144 fluent Arabic descriptions. An analysis of the translation errors is provided and organized into an insightful taxonomy to guide future Arabic localization efforts. We train a CLIP-style baseline with identical hyperparameters on the Arabic and English variants of the benchmark, finding a moderate performance gap (𝛥 ≈ 3pp at Recall@1), indicating that Arabic localization preserves benchmark difficulty. We evaluate three post-editing budgets (zero/ flagged-only/ full) and find that performance improves monotonically with more post-editing, while the raw LLM output (zero-budget) remains usable. To ensure reproducibility to other languages, we made the code available at https://github.com/Tahaalshatiri/AutoArabic.

Arabic presents unique challenges for sense level language understanding due to its rich morphology and semantic ambiguity. This paper benchmarks large generative language models (LLMs) for Arabic Word Sense Disambiguation (WSD) under both zero-shot and fine-tuning conditions. We evaluate one proprietary model (GPT-4o) and three opensource models (LLaMA 3.1-8B, Qwen 2.5-7B, and Gemma 2-9B) on two publicly available datasets. In zero-shot settings, GPT-4o achieved the highest overall performance, with comparable results across both datasets, reaching 79% accuracy and an average macro-F1 score of 66.08%. Fine-tuning, however, notably elevated all open models beyond GPT4o’s zero-shot results. Qwen achieved the top scores on one dataset, with an accuracy of 90.77% and a macro-F1 score of 83.98%, while LLaMA scored highest on the other, reaching an accuracy of 88.51% and a macroF1 score of 69.41%. These findings demonstrate that parameter-efficient supervised adaptation can close much of the performance gap and establish strong, reproducible baselines for Arabic WSD using open-source, relatively medium-sized models. Full code is publicly available.

We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we introduce a novel language adaptation approach by leveraging the Branch-Train-MiX strategy to merge script-specialized experts, into a single MoE model. Our Nile-Chat models significantly outperform leading multilingual and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced Egyptian evaluation benchmarks, which span both understanding and generative tasks. Notably, our 12B model delivers a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly available. We believe this work presents a comprehensive methodology for adapting LLMs to a single language with dual-script usage, addressing an often overlooked aspect in contemporary LLM development.

Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., Persona and System Prompts); (3) Alignment (e.g., Cultural, Safety, Ethics, and Fairness); and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic-centric LLMs and applications while providing concrete recommendations for future efforts in Arabic post-training dataset development.

The linguistic fragmentation of Arabic, with over 30 dialects exhibiting low mutual intelligibility, presents a critical challenge for deploying natural language processing (NLP) in healthcare. Conventional fine-tuning of large language models (LLMs) for each dialect is computationally prohibitive and operationally unsustainable. In this study, we explore model merging as a scalable alternative by integrating three pre-trained LLMs—a medical domain expert, an Egyptian Arabic model, and a Moroccan Darija model—into a unified system without additional fine-tuning. We introduce a novel evaluation framework that assesses both dialectal fidelity via dual evaluation: LLM-based automated scoring and human assessments by native speakers. Our results demonstrate that the merged model effectively handles cross-dialect medical scenarios, such as interpreting Moroccan Darija inputs for Egyptian Arabic-speaking clinicians, while maintaining high clinical relevance. The merging process reduced computational cost by over 60% compared to per-dialect fine-tuning, highlighting its viability for resource-constrained settings. This work offers a promising path for building dialect-aware medical LLMs at scale, with implications for broader deployment across linguistically diverse regions.

Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer, (2) the effect of general-purpose instruction tuning on tool-calling performance, and (3) the value of fine-tuning on specific, high-priority tools. To address these questions, we conduct extensive experiments using base and post-trained variants of an open-weight Arabic LLM. To enable this study, we bridge the resource gap by translating and adapting two open-source tool-calling datasets into Arabic. Our findings provide crucial insights into the optimal strategies for developing robust tool-augmented agents for Arabic.

Despite the growing importance of online discourse, Arabic-speaking communities lack platforms that support structured, culturally grounded debate. Mainstream social media rarely fosters constructive engagement, often leading to polarization and superficial exchanges. This paper proposes the development of a culturally aware debate platform tailored to the values and traditions of Arabic-speaking users, with a focus on leveraging advances in natural language processing (NLP). We present findings from a user survey that explores experiences with existing debate tools and expectations for future platforms. Besides, we analyze 30,000 English-language debate topics using large language models (LLMs) to assess their cultural relevance and appropriateness for Arab audiences. We further examine the ability of LLMs to generate new culturally resonant debate topics, contributing to the emerging tasks of culture-aware topic assessment and generation. Finally, we propose a theoretical and technical framework for building an NLP-supported Arabic debate platform. Our work highlights the urgent need for culturally sensitive NLP resources that foster critical thinking, digital literacy, and meaningful deliberation in Arabic.

Processing North African Arabic dialects presents significant challenges due to high lexical variability, frequent code-switching with French, and the use of both Arabic and Latin scripts. We address this with a phonemebased normalization strategy that maps Arabic and French text into a simplified representation (Arabic rendered in Latin script), reflecting native reading patterns. Using this method, we pretrain BERTbased models on normalized Modern Standard Arabic and French only and evaluate them on Named Entity Recognition (NER) and text classification. Experiments show that normalized standard-language corpora yield competitive performance on North African dialect tasks; in zero-shot NER, Ar_20k surpasses dialectpretrained baselines. Normalization improves vocabulary alignment, indicating that normalized standard corpora can suffice for developing dialect-supportive

We address the task of reverse dictionary modeling in Arabic, where the goal is to retrieve a target word given its definition. The task comprises two subtasks: (1) generating embeddings for Arabic words based on Arabic glosses, and (2) a cross-lingual setting where the gloss is in English and the target embedding is for the corresponding Arabic word. Prior approaches have largely relied on BERT models such as CAMeLBERT or MARBERT trained with mean squared error loss. In contrast, we propose a novel ensemble architecture that combines MARBERTv2 with the encoder of AraBART, and we demonstrate that the choice of loss function has a significant impact on performance. We apply contrastive loss to improve representational alignment, and introduce structural and center losses to better capture the semantic distribution of the dataset. This multi-loss framework enhances the quality of the learned embeddings and leads to consistent improvements in both monolingual and cross-lingual settings. Our system achieved the best rank metric in both subtasks compared to the previous approaches. These results highlight the effectiveness of combining architectural diversity with task-specific loss functions in representational tasks for morphologically rich languages like Arabic.

We introduce ALARB, a dataset and suite of tasks designed to evaluate the reasoning capabilities of large language models (LLMs) within the Arabic legal domain. While existing Arabic benchmarks cover some knowledge-intensive tasks such as retrieval and understanding, substantial datasets focusing specifically on multistep reasoning for Arabic LLMs, especially in open-ended contexts, are lacking. The dataset comprises over 13K commercial court cases from Saudi Arabia, with each case including the facts presented, the reasoning of the court, the verdict, as well the cited clauses extracted from the regulatory documents. We define a set of challenging tasks leveraging this dataset and reflecting the complexity of real-world legal reasoning, including verdict prediction, completion of reasoning chains in multistep legal arguments, and identification of relevant regulations based on case facts. We benchmark a representative selection of current open and closed Arabic LLMs on these tasks and demonstrate the dataset’s utility for instruction tuning. Notably, we show that instruction tuning a modest 12B parameter model using ALARB significantly enhances its performance in verdict prediction and Arabic verdict generation, reaching a level comparable to that of GPT-4o.

Argument mining for Arabic remains underexplored, largely due to the scarcity of annotated corpora. To address this gap, we examine the effectiveness of cross-lingual transfer from English. Using the English Persuasive Essays (PE) corpus, annotated with argumentative components (Major Claim, Claim, and Premise), we explore several transfer strategies: training encoder-based multilingual and monolingual models on English data, machine-translated Arabic data, and their combination. We further assess the impact of annotation noise introduced during translation by manually correcting portions of the projected training data. In addition, we investigate the potential of prompting large language models (LLMs) for the task. Experiments on a manually corrected Arabic test set show that monolingual models trained on translated data achieve the strongest performance, with further improvements from small-scale manual correction of training examples.

While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research.

We present Octopus, a first family of modular speech-language models designed for Arabic-English ASR, dialect identification, and speech translation. Built on Whisper-V3 and enhanced with large language models like ALLaM, LLaMA, and DeepSeek, Octopus bridges speech and text through a lightweight projection layer and Q-Former. To broaden its scope beyond speech, Octopus integrates BEATs, a general-purpose audio encoder allowing it to understand both linguistic and acoustic events. Despite its simplicity, this dual-encoder design supports robust performance across multilingual and code-switched scenarios. We also introduce TinyOctopus, a distilled variant using smaller models (Distil-Whisper + LLaMA3-1B / DeepSeek-1.5B), achieving competitive results with just a fraction of the parameters. Fine-tuning on synthetic code-switched data further boosts its performance. Octopus demonstrates the power of compact, extensible architectures in Arabic-centric speech modeling and sets the stage for unified multilingual audio-language understanding.

The quality of training data plays a critical role in the performance of large language models (LLMs). This is especially true for low-resource languages where high-quality content is relatively scarce. Inspired by the success of FineWeb-Edu for English, we construct a native Arabic educational-quality dataset using similar methodological principles. We begin by sampling 1 million Arabic web documents from Common Crawl and labeling them into six quality classes (0–5) with Qwen-2.5-72B-Instruct model using a classification prompt adapted from FineWeb-Edu. These labeled examples are used to train a robust classifier capable of distinguishing educational content from general web text. We train a classification head on top of a multilingual 300M encoder model, then use this classifier to filter a large Arabic web corpus, discarding documents with low educational value. To evaluate the impact of this curation, we pretrain from scratch two bilingual English-Arabic 7B LLMs on 800 billion tokens using the filtered and unfiltered data and compare their performance across a suite of benchmarks. Our results show a significant improvement when using the filtered educational dataset, validating the effectiveness of quality filtering as a component in a balanced data mixture for Arabic LLM development. This work addresses the scarcity of high-quality Arabic training data and offers a scalable methodology for curating educational quality content in low-resource languages.

In this paper, we present the Arabic Multimodal Crawl (AMCrawl), the first native-based Arabic multimodal dataset to our knowledge, derived from the Common Crawl corpus and rigorously filtered for quality and safety. Image-text pair datasets are the standard choice for pretraining multimodal large language models. However, they are often derived from image alt-text metadata, which is typically brief and context-poor, disconnecting images from their broader meaning. Although significant advances have been made in building interleaved image-text datasets for English, such as the OBELICS dataset, a substantial gap remains for native Arabic content. Our processing covered 8.6 million Arabic web pages, yielding 5.8 million associated images and 1.3 billion text tokens. The final dataset includes interleaved image-text documents and question-answer pairs, featuring 2.8 million high-quality interleaved documents and 5 million QA pairs. Alongside the dataset, we release the complete pipeline and code, ensuring reproducibility and encouraging further research and development. To demonstrate the effectiveness of AMCrawl, we introduce a publicly available native Arabic Vision Language model, trained with 13 billion parameters. These models achieve competitive results when benchmarked against publicly available datasets. AMCrawl bridges a critical gap in Arabic multimodal resources, providing a robust foundation for developing Arabic multimodal large language models and fostering advancements in this underrepresented area. Code: github.com/shahad-aboukozzana/AMCrawl

Grapheme-to-phoneme (G2P) models are essential components in text-to-speech (TTS) and pronunciation assessment applications. While standard forms of languages have gained attention in that regard, dialectal speech, which often serves as the primary means of spoken communication for many communities, as it is the case for Arabic, has not received the same level of focus. In this paper, we introduce an end-to-end dialectal G2P for Egyptian Arabic, a dialect without standard orthography. Our novel architecture accomplishes three tasks: (i) restores short vowels of the diacritical marks for the dialectal text; (ii) maps certain characters that happen only in the spoken version of the dialectal Arabic to their dialect-specific character transcriptions; and finally (iii) converts the previous step output to the corresponding phoneme sequence. We benchmark G2P on a modular cascaded system, a large language model, and our multi-task end-to-end architecture.

Content-grounded dialogue evaluation for Arabic remains under-resourced, particularly across Modern Standard (MSA), Egyptian, and Maghrebi varieties. We introduce Shawarma Chats, a benchmark of 30,000 six-turn conversations grounded in Wikipedia content, evenly split across the three dialects. To build this corpus, we prompt five frontier LLMs GPT-4o, Gemini 2.5 Flash, Qwen-Plus, DeepSeek-Chat, and Mistral Large to generate 1,500 seed dialogues. Native Arabic speakers evaluate these outputs to select the most effective generator and most human-aligned grader. Sub-A dialogues undergo a two-pass, rationale-driven self-repair loop where the grader critiques and the generator revises; unresolved cases are manually corrected. We apply this pipeline to 10,000 Wikipedia paragraphs to create 30,000 high-quality conversations 10,000 per dialect—at modest human cost. To validate the benchmark, we LoRA-fine-tune six open LLMs (1–24 B parameters) on Shawarma Chats and observe consistent gains in automatic-grader scores, BERTScore, BLEU and ROUGE particularly for models larger than 7 B parameters. Shawarma Chats thus establishes the first large-scale, dialect-aware, content-grounded dialogue benchmark for Arabic.