Oral Presentation: When the Model Shouldn't Cite Calibrated Abstention for Islamic Citation Hallucination, by Dr. Ahmad Rushdi (Stanford University)

Large language models sometimes misquote the Qur'anic verses and Hadiths they cite --- a high-stakes failure where confidently fabricating sacred text is worse than ordinary hallucination. The 2025 IslamicEval shared task benchmarks this, but every system must commit a label on every span; none abstains. We add a plug-in abstention layer: combine publicly released LLM outputs with cross-family runs of our own (K=7 total), score each span by majority-disagreement, and abstain when disagreement is high. The threshold is chosen on a 60/40 calibration split via a union-bound-adjusted Clopper-Pearson upper bound, giving a calibration-sample high-probability bound on selective risk. On the Subtask 1C dev set, the selective-accuracy curve exceeds the leaderboard reference at 14% abstention and reaches 94% conditional accuracy at 44%; risk-controlled operating points certify the 44% region. Public test labels are unavailable, so leaderboard scores serve as reference lines, not head-to-head. The layer is plug-and-play, requires no retraining, and is reusable across tasks.