We present HalluScoring 2026, a shared task for evaluating hallucination detection and factual verification in Arabic question answering under challenging generalization settings. The shared task is organized into two main tasks, each comprising two subtasks, for a total of four subtasks. Task 1 evaluates binary hallucination detection, considering generalization to unseen questions (Subtask 1.1) and responses generated by unseen LLMs (Subtask 1.2). Task 2 extends the evaluation beyond detection by requiring the systems to additionally identify the correct factual answer from six related candidates, covering Islamic knowledge (Subtask 2.1) and general knowledge (Subtask 2.2). The shared task is based on two Arabic datasets: HalluScore and HalluTruthQA. A total of 13 teams participated in the shared task, 10 of which submitted system description papers. The results of Task 1 demonstrate that hallucination detection remains challenging under distribution shift, with the winning team achieving AUC-ROC test scores of 0.772 and 0.767 for Subtasks 1.1 and 1.2, respectively. For Task 2, the winning team achieved scores of 0.882 and 0.857 in the Islamic and general-knowledge subtasks, respectively, under assisted evaluation.

Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing hallucination benchmarks often provide only response-level labels, offering limited support for identifying the exact erroneous content, explaining why it is incorrect, or recovering the correct factual answer. We introduce HalluTruthQA, a fine-grained benchmark for hallucination evaluation in Arabic question answering. HalluTruthQA contains 2,400 expert-curated examples across four knowledge-intensive domains: Islamic knowledge, history, science, and geography. Each example includes an Arabic question, a model-generated answer, a verified reference answer, a binary hallucination label, and six candidate answers for factual verification. For hallucinated answers, the benchmark additionally provides character-level erroneous spans, human-written explanations, and macro- and micro-level hallucination types. We evaluate four open-source LLMs—ALLaM-7B, Falcon-H1R-7B, Qwen3-32B, and SILMA—in a zero-shot setting across four tasks: hallucination detection, span-level localization, factual verification, and explanation evaluation. The results show that these tasks measure complementary capabilities, with no single model achieving the best performance across all of them. The best observed scores are 0.880 Macro-F1 for hallucination detection, 0.516 F1-Sp for span localization, 0.852 LO-Score for factual verification, and 0.644 for explanation evaluation. Our findings highlight the importance of moving beyond binary hallucination detection toward fine-grained evaluation that also localizes, verifies, categorizes, and explains factual errors. Code, data, prompts, and evaluation scripts are available at https://gitlab.com/nlpresearcher/HalluTruthQA