Large Language Models (LLMs) have demonstrated remarkable performance across numerous natural language understanding use cases. However, this impressive performance comes with inherent limitations, such as the tendency to perpetuate stereotypical biases or fabricate non-existent facts. In the context of Islam and its representation, accurate and factual representation of its beliefs and teachings rooted in the Quran and Sunnah is key. This work focuses on the challenge of building domain-specific LLMs faithful to the Islamic worldview and proposes ways to build and evaluate such systems. Firstly, we define this open-ended goal as a technical problem and propose various solutions. Subsequently, we critically examine known challenges inherent to each approach and highlight evaluation methodologies that can be used to assess such systems. This work highlights the need for high-quality datasets, evaluations, and interdisciplinary work blending machine learning with Islamic scholarship.

翻译：大语言模型在众多自然语言理解任务中展现出了卓越性能。然而，这种令人瞩目的表现伴随着固有局限——例如倾向于强化刻板偏见或捏造不实信息。在伊斯兰教及其表征的语境下，准确真实地呈现根植于《古兰经》与圣训的信仰与教义至关重要。本研究聚焦于构建忠实于伊斯兰世界观的领域特定大语言模型这一挑战，并提出构建与评估此类系统的可行方案。首先，我们将这一开放性目标定义为技术问题，并提出多种解决方案。随后，我们批判性审视每种方法面临的已知挑战，重点阐释可用于评估此类系统的评价方法论。本工作凸显了高质量数据集、评估体系以及融合机器学习与伊斯兰学术的跨学科研究的必要性。