Large language models are increasingly used to answer questions about religious texts, yet their fluent responses may contain misquoted, misattributed, or fabricated citations. Detecting such hallucinations requires identifying citation components, verifying their correctness, and recovering the canonical text when a quotation is incorrect. We present Yaqeen’s submission to the IslamicEval 2026 shared task, which evaluates these capabilities through three subtasks: detection of Qur’anic verses, Hadith text, chains of narration, and claimed sources; identification of incorrect citation fragments; and correction of erroneous Qur’anic and Hadith quotations. We develop task-specific systems combining AraBERT-based character-level sequence labeling with retrieval-based matching, classification, and reranking. Our systems achieve 96.78% character-level Macro-F1 on Task 1, 91.07% accuracy on Task 2, and 78.02% accuracy on Task 3. The results show that combining contextual Arabic representations with retrieval-based evidence is effective for fine-grained hallucination detection and correction in Arabic Islamic content.

Authorship attribution is a critical task in natural language processing with applications ranging from forensic linguistics to plagiarism detection. While well-studied in high-resource languages, it remains challenging for low-resource languages like Arabic and Urdu. In this paper, we present our participation in the AbjadNLP shared task, where we systematically evaluate three distinct approaches: traditional machine learning using SVM with TF-IDF features, fine-tuned transformer-based models (AraBERT), and LLMs. We demonstrate that while fine-tuned AraBERT excels in Arabic, traditional lexical models (SVM) prove more robust for Urdu, outperforming both BERT-based and LLM approaches. We also show that few-shot prompting with LLMs, when operated as a reranker over top candidates, significantly outperforms zero-shot baselines. Our final systems achieved competitive performance, ranking 6th and 1st in the Arabic and Urdu tasks respectively.