GenAI4ELab/papercli-papers huggingface dataset:
WojoodOntology: Ontology-Driven LLM Prompting for Unified Information Extraction Tasks
Authors: Alaa Aljabari; Nagham Hamad; Mohammed Khalilia; Mustafa Jarrar
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Tahḏīb: A Rhythm-Aware Phrase Insertion for Classical Arabic Poetry Composition
Authors: Mohamad Elzohbi; Richard Zhao
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Can LLMs Directly Retrieve Passages for Answering Questions from Qur’an?
Authors: Sohaila Eltanbouly; Salam Albatarni; Shaimaa Hassanein; Tamer Elsayed
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Authors: Ali Abouzeid; Bilal Elbouardi; Mohamed Maged; Shady Shehata
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of Cultural and Gender Dimensions
Authors: Houda Bouamor; Sara Al-Emadi; Zeinab Ibrahim; Hany Fazzaa; Aisha Al-Sultan
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Feature Engineering is not Dead: A Step Towards State of the Art for Arabic Automated Essay Scoring
Authors: Marwan Sayed; Sohaila Eltanbouly; May Bashendy; Tamer Elsayed
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation
Authors: Abdessalam Bouchekif; Samer Rashwani; Heba Sbahi; Shahd Gaben; Mutaz Al Khatib; Mohammed Ghaly
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
BALSAM: A Platform for Benchmarking Arabic Large Language Models
Authors: Rawan Al-Matham; Kareem Darwish; Raghad Al-Rasheed; Waad Alshammari; Muneera Alhoshan; Amal Almazrua; Asma Al Wazrah; Mais Alheraki; Firoj Alam; Preslav Nakov; Norah Alzahrani; Eman AlBilali; Nizar Habash; Abdelrahman El-Sheikh; Muhammad Elmallah; Haonan Li; Hamdy Mubarak; Mohamed Anwar; Zaid Alyafeai; Ahmed Abdelali; Nora Altwairesh; Maram Hasanain; Abdulmohsen Al Thubaity; Shady Shehata; Bashar Alhafni; Injy Hamed; Go Inoue; Khalid Elmadani; Ossama Obeid; Fatima Haouari; Tamer Elsayed; Emad Alghamdi; Khalid Almubarak; Saied Alshahrani; Ola Aljarrah; Safa Alajlan; Areej Alshaqarawi; Maryam Alshihri; Sultana Alghurabi; Atikah Alzeghayer; Afrah Altamimi; Abdullah Alfaifi; Abdulrahman AlOsaimy
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English
Authors: Fethi Bougares; Salima Mdhaffar; Haroun Elleuch; Yannick Estève
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
AutoArabic: A Three-Stage Framework for Localizing Video-Text Retrieval Benchmarks
Authors: Mohamed Eltahir; Osamah Sarraj; Abdulrahman M. Alfrihidi; Taha Alshatiri; Mohammed Khurd; Mohammed Bremoo; Tanveer Hussain
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Zero-Shot and Fine-Tuned Evaluation of Generative LLMs for Arabic Word Sense Disambiguation
Authors: Yossra Noureldien; Abdelrazig Mohamed; Farah Attallah
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
Authors: Guokan Shang; Hadi Abdine; Ahmad Chamma; Amr Mohamed; Mohamed Anwar; Abdelaziz Bounhar; Omar El Herraoui; Preslav Nakov; Michalis Vazirgiannis; Eric P. Xing
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations
Authors: Mohammed Alkhowaiter; Norah Alshahrani; Saied Alshahrani; Reem I. Masoud; Alaa Alzahrani; Deema Alnuhait; Emad A. Alghamdi; Khalid Almubarak
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Bridging Dialectal Gaps in Arabic Medical LLMs through Model Merging
Authors: Ahmed Ibrahim; Abdullah Hosseini; Hoda Helmy; Wafa Lakhdhar; Ahmed Serag
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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 for Arabic LLMs: Data Strategies and Instruction Tuning
Authors: Asım Ersoy; Enes Altinisik; Kareem Mohamed Darwish; Husrev Taha Sencar
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Toward Culturally-Aware Arabic Debate Platforms with NLP Support
Authors: Khalid Al Khatib; Mohammad Khader
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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.
Modeling North African Dialects from Standard Languages
Authors: Yassine Toughrai; Kamel Smaïli; David Langlois
Venue: EMNLP 2025 (Proceedings of The Third Arabic Natural Language Processing Conference)

Abstract: 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