Stance detection identifies an author’s position toward a specified target, yet Arabic social-media text remains challenging because stance is often implicit and shaped by dialect, sarcasm, informal expression, and culturally specific references. StanceEval-2026 evaluates this problem in two transfer settings: a related target within a seen thematic domain (Track 1) and entirely unseen topics (Track 2). We compare four modeling strategies: single-task Arabic encoder fine-tuning, multi-task learning with sentiment and sarcasm supervision, 4-bit QLoRA adaptation of the ALLaM-7B model, and Retrieval-Augmented In-Context Learning (RAG-ICL) with frozen large language models (LLMs). Our strongest single-backend configuration is a data-centric RAG-ICL pipeline combining label-balanced retrieval, hashtag and negation adaptation, text cleaning, similarity filtering, and target descriptions. A majority-vote ensemble of this configuration across three LLM backends obtains official F_avg2 scores of 0.8719 on Track 1 and 0.9016 on Track 2, ranking 3rd and 7th, respectively. Under the shared-task test conditions, the retrieval-based systems substantially outperform the supervised systems evaluated here, particularly on Track 2.

We describe SynAnchor, Nasaq’s definition-guided system for AraGenre 2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers’ definition-embedding baseline (approximately 2.4). Approximately 25% of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories.

We introduce Definition-Guided Learning (DGL), a contrastive training framework that uses dictionary definitions as semantic queries and lemmas as retrieval targets, instantiated here for Arabic reverse dictionary retrieval. We construct a root-stratified Arabic lexical benchmark that limits morphological root overlap between training and test partitions. Zero-shot evaluation suggests a notable gap, with the best pre-trained model achieving only 11.16% Top@1 accuracy on this benchmark. Fine-tuning with DGL yields 41.13% Top@1 and NDCG@10 of 49.32%, an improvement of roughly 3.7 times over the strongest zero-shot baseline we tested. Our results suggest that in-batch negative sampling tends to outperform Triplet Loss in this setting, and that definition length and lexical difficulty appear to trade off in importance across retrieval depth. These findings offer preliminary evidence that lexicographic resources can serve as a useful supervisory signal for Arabic embedding learning, at least within the scope of the benchmark studied here.

Arabic machine translation benchmarks provide limited coverage of variation within Saudi Arabic. We introduce SAFIR, a Saudi-centric benchmark of 610 unique sentence pairs organized into 12 creator specific evaluation splits across five regional groups: Najdi, Hijazi, Southern, Northern, and Eastern. Native speakers authored the Arabic sentences and their English translations; 235 pairs received independent double review for dialect authenticity, translation adequacy, and fluency. We evaluate twelve systems, comprising losed-weight LLMs, open-weight LLMs, and dedicated MT models, in the zero-shot Saudi→English setting using reference-based metrics and GPT-4o judgments. Closed-weight LLMs obtain the strongest aggregate results. Gemini 2.5 Pro achieves the highest average BLEU (34.41), while GPT-5 achieves the highest BERTScore (83.60). Rankings vary across splits, and Southern varieties present particular challenges in judged adequacy. Differences between reference overlap, adequacy, and fluency support reporting performance by sampled variety and evaluation dimension. SAFIR and its leaderboard support more detailed assessment of Saudi Arabic translation.

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding. Constructed through advanced agentic workflows and extensive human-in-the-loop annotations by 37 annotators from across the Arab world, PEARL comprises over 309K multimodal examples spanning ten culturally significant domains covering all Arab countries. We further provide two robust evaluation benchmarks (PEARL and PEARL-LITE) along with a specialized subset (PEARL-X) explicitly developed to assess nuanced cultural variations. Comprehensive evaluations on state-of-the-art open and proprietary LVLMs demonstrate that reasoning-centric instruction alignment substantially improves models’ cultural grounding compared to conventional scaling methods. PEARL establishes a foundational resource for advancing culturally-informed multimodal modeling research. All datasets and benchmarks are publicly available.

DA-MSA Machine Translation is a recentchallenge due to the multitude of Arabic dialects and their variations. In this paper, we present our results within the context of Subtask 3 of the NADI-2024 Shared Task(Abdul-Mageed et al., 2024) that is DA-MSA Machine Translation . We utilized the DIALECTS008MSA MADAR corpus (Bouamor et al., 2018),the Emi-NADI corpus for the Emirati dialect (Khered et al., 2023), and we augmented thePalestinian and Jordanian datasets based onNADI 2021. Our approach involves develop013ing sentence-level machine translations fromPalestinian, Jordanian, Emirati, and Egyptiandialects to Modern Standard Arabic (MSA).To016 address this challenge, we fine-tuned models such as (Nagoudi et al., 2022)AraT5v2-msa-small, AraT5v2-msa-base, and (Elmadanyet al., 2023)AraT5v2-base-1024 to comparetheir performance. Among these, the AraT5v2-base-1024 model achieved the best accuracy, with a BLEU score of 0.1650 on the develop023ment set and 0.1746 on the test set.

Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.

This paper describes our participation in the ArAIEval Shared Task 2024, focusing on Task 2C, which challenges participants to detect propagandistic elements in multimodal Arabic memes. The challenge involves analyzing both the textual and visual components of memes to identify underlying propagandistic messages. Our approach integrates the capabilities of MARBERT and ResNet50, top-performing pre-trained models for text and image processing, respectively. Our system architecture combines these models through a fusion layer that integrates and processes the extracted features, creating a comprehensive representation that is more effective in detecting nuanced propaganda. Our proposed system achieved significant success, placing second with an F1 score of 0.7987.

The rapid proliferation of disinformation through social media has become one of the most dangerous means to deceive and influence people’s thoughts, viewpoints, or behaviors due to social media’s facilities, such as rapid access, lower cost, and ease of use. Disinformation can spread through social media in different ways, such as fake news stories, doctored images or videos, deceptive data, and even conspiracy theories, thus making detecting disinformation challenging. This paper is a part of participation in the ArAIEval competition that relates to disinformation detection. This work evaluated four models: MARBERT, the proposed ensemble model, and two tests over GPT-4 (zero-shot and Few-shot). GPT-4 achieved micro-F1 79.01% while the ensemble method obtained 76.83%. Despite no improvement in the micro-F1 score on the dev dataset using the ensemble approach, we still used it for the test dataset predictions. We believed that merging different classifiers might enhance the system’s prediction accuracy.

A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the Semi-Decoder model for our target task. Our system secured 2nd place based on the Rank metric for both embeddings (Electra and Sgns).