We present KnowledgeGraphEval 2026, the first Arabic shared task dedicated to knowledge graph (KG) construction from text. The task focuses on extracting named entities and semantic relations from Arabic text to build structured, machine-readable knowledge representations. It includes two subtasks: (i) AdaptNER, which evaluates cross-domain named entity recognition (NER), and (ii) Relation Extraction (RE), which predicts semantic relations between predefined entity pairs. For AdaptNER, we release KONOOZ MSA, an Arabic NER benchmark annotated with 21 entity types across 10 domains. For RE, we release WojoodRelations, an Arabic RE dataset annotated with 40 relation types. At test time, 14 teams participated, of which 13 submitted system description papers, and one RE paper was subsequently rejected. The best systems achieve F1 scores of 76% and 96% on AdaptNER and RE, respectively. Results show that cross-domain NER remains challenging, while RE systems continue to face difficulties with rare and semantically confusable relations. We release the datasets, baselines, and evaluation tools for reproducible research in Arabic information extraction and KG construction.

Adversarial training is widely acknowledged as the most effective defense against adversarial attacks. However, it is also well established that achieving both robustness and generalization in adversarially trained models involves a trade-off. The goal of this work is to provide an in depth comparison of different approaches for adversarial training in language models. Specifically, we study the effect of pre-training data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of transformer-based language models. Our findings suggest that better robustness can be achieved by pre-training data augmentation or by training with input space perturbation. However, training with embedding space perturbation significantly improves generalization. A linguistic correlation analysis of neurons of the learned models reveal that the improved generalization is due to ‘more specialized’ neurons. To the best of our knowledge, this is the first work to carry out a deep qualitative analysis of different methods of generating adversarial examples in adversarial training of language models.