Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI
2026-06-15 • Computation and Language
Computation and Language
AI summaryⓘ
The authors review how large language models (LLMs) are used to answer complex Islamic questions, which need accurate sources, precise citations, and respect for different legal opinions. They explain that just knowing Arabic isn’t enough for trustworthy Islamic AI; systems also need special tools to find, check, and reference reliable information properly. The survey covers topics like Arabic natural language processing, Islamic legal reasoning, and evaluating when models make mistakes. Finally, the authors suggest future research to create Islamic AI that avoids false answers and maintains trust.
Large Language ModelsIslamic AIArabic NLPQur'anic Question AnsweringRetrieval-Augmented GenerationMadhhabHallucination EvaluationLegal ReasoningFaithfulnessCitation-Aware Generation
Authors
Mohammed Amine Mouhoub
Abstract
Large language models (LLMs) are increasingly used for knowledge-intensive question answering, including religious and legal questions. Islamic knowledge is a particularly demanding setting: answers are expected to be grounded in authoritative sources, citations must be exact, Arabic varieties differ substantially from the language of classical sources, and legitimate jurisprudential disagreement must be represented rather than collapsed into a single answer. This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI. We organize the literature around Arabic NLP and Arabic-centric LLMs, Islamic NLP resources, Qur'anic question answering, Islamic knowledge benchmarks, retrieval-augmented generation, Islamic legal reasoning, inheritance reasoning, hallucination evaluation, and trustworthiness. We argue that fluency in Arabic is not sufficient for Islamic AI. Reliable systems require curated sources, retrieval and verification modules, citation-aware generation, madhhab-aware reasoning, human expert evaluation, and benchmarks that measure not only answer accuracy but also faithfulness, source validity, and reasoning quality. The survey concludes with a research agenda for hallucination-resistant Islamic AI systems.