构建可信伊斯兰大模型需兼顾经文引用与教法学派差异
Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI
- 聚焦阿拉伯语NLP与伊斯兰知识库，构建可信AI系统
- 强调经文引用准确性和教法学派差异的差异化表达
- 适合宗教、法律领域研究者及可信AI开发者参考
大型语言模型在知识密集型问答中应用日益广泛，尤其涉及宗教与法律问题。伊斯兰知识尤为复杂：答案须基于权威来源，引用必须精确，现代阿拉伯语与古典经文语言差异显著，且需体现合法教法学派分歧而非单一答案。本文综述伊斯兰大模型与可信伊斯兰AI领域的进展，涵盖阿拉伯语NLP、阿拉伯语中心的大模型、伊斯兰NLP资源、古兰经问答、伊斯兰知识基准、检索增强生成、伊斯兰法律推理、继承推理、幻觉评估与可信性。指出单纯掌握阿拉伯语不足以实现可靠系统，需依赖精心整理的来源、检索验证模块、引用感知生成、学派意识推理、专家评估及综合衡量答案准确性、忠实性、来源有效性与推理质量的基准。最后提出抗幻觉伊斯兰AI的研究议程。
原文摘要 · Abstract (English)
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.
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