We present an overview of the AdabEval 2026 shared task, organized as part of the OSACT7 workshop (co-located with LREC 2026). This task introduces the first benchmark suite for politeness detection. It includes two subtasks: Politeness Classification (Subtask A) and Category Prediction (Subtask B). The task focuses on evaluating models’ ability to recognize and categorize politeness phenomena in Arabic text. Evaluation was conducted using an automatic metric (macro F1-score). A total of 28 unique teams participated in the shared task. Of these, 13 teams submitted final system predictions across the two subtasks. The top-performing systems relied primarily on transformer-based architectures. The winning systems achieved macro F1-scores of 0.89 for Subtask A and 0.58 for Subtask B.

The growing importance of culturally-aware natural language processing systems has led to an increasing demand for resources that capture sociopragmatic phenomena across diverse languages. Nevertheless, Arabic-language resources for politeness detection remain severely under-explored, despite the rich and complex politeness expressions deeply embedded in Arabic communication. In this paper, a new annotated Arabic dataset, called ADAB/أدب (Arabic Politeness Dataset), was generated and carefully collected from four diverse online platforms including social media, e-commerce, and customer service domains, encompassing both Modern Standard Arabic (MSA) and multiple dialectal varieties (Gulf, Egyptian, Levantine, and Maghrebi). This dataset has undergone a thorough annotation process guided by Arabic linguistic traditions and contemporary pragmatic theory, resulting in three-way politeness classifications: polite, impolite, and neutral. The generated dataset contains 10,000 samples with detailed linguistic feature annotations across 16 politeness categories, achieving substantial inter-annotator agreement (κ = 0.703). A comprehensive benchmarking of this dataset was conducted utilizing 40 model configurations spanning traditional machine learning (12 models), transformer-based architecture (10 models), and large language models (18 configurations), thereby effectively demonstrating its practical utility and inherent challenges. This generated resource aims to bridge the gap in Arabic sociopragmatic NLP and encourage further research into politeness-aware applications for the Arabic language.