Building Domain-Specific LLMs Faithful To The Islamic Worldview: Mirage or Technical Possibility?
This work addresses the problem of biased or inaccurate AI representations in Islam for researchers and practitioners, proposing interdisciplinary approaches but is incremental as it builds on existing LLM frameworks.
The paper tackles the challenge of creating domain-specific large language models (LLMs) that accurately represent the Islamic worldview, focusing on solutions and evaluation methods to address biases and factual inaccuracies.
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.