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.

构建与伊斯兰世界观一致的领域特定大型语言模型 (Large Language Models) 的挑战、解决方案和评估方法的研究。

构建与伊斯兰世界观相符的领域专用 LLMs：幻觉还是技术可能性？

Building Domain-Specific LLMs Faithful To The Islamic Worldview: Mirage  or Technical Possibility?

In Islam, readers must apply a set of pronunciation rules called Tajweed
rules to recite the Quran in the same way that the angel Jibrael taught the
Prophet, Muhammad. The traditional process of learning the correct application
of these rules requires a human who must have a license and great experience to
detect mispronunciation. Due to the increasing number of Muslims around the
world, the number of Tajweed teachers is not enough nowadays for daily
recitation practice for every Muslim. Therefore, lots of work has been done for
automatic Tajweed rules' mispronunciation detection to help readers recite
Quran correctly in an easier way and shorter time than traditional learning
ways. All previous works have three common problems. First, most of them
focused on machine learning algorithms only. Second, they used private datasets
with no benchmark to compare with. Third, they did not take into consideration
the sequence of input data optimally, although the speech signal is time
series. To overcome these problems, we proposed a solution that consists of
Mel-Frequency Cepstral Coefficient (MFCC) features with Long Short-Term Memory
(LSTM) neural networks which use the time series, to detect mispronunciation in
Tajweed rules. In addition, our experiments were performed on a public dataset,
the QDAT dataset, which contains more than 1500 voices of the correct and
incorrect recitation of three Tajweed rules (Separate stretching , Tight Noon ,
and Hide ). To the best of our knowledge, the QDAT dataset has not been used by
any research paper yet. We compared the performance of the proposed LSTM model
with traditional machine learning algorithms used in SoTA. The LSTM model with
time series showed clear superiority over traditional machine learning. The
accuracy achieved by LSTM on the QDAT dataset was 96%, 95%, and 96% for the
three rules (Separate stretching, Tight Noon, and Hide), respectively.

本文提出了一种使用 MFCC 和 LSTM 网络结合时序序列的自动 Tajweed 规则误读检测方法，用于帮助更多的穆斯林正确地诵读古兰经。在公共数据集 QDAT 上测试，LSTM 模型的准确率分别为 96％、95％和 96％，表现优于传统机器学习算法。