In December 2024, a groundbreaking research paper titled “A Novel LLM-Based Approach for Automated Seerah-Hadith Mapping: Connecting Islamic Historical Narratives Through Vector Search and Semantic Analysis” was presented at the Muslims in ML Workshop 2024, co-located with NeurIPS’24 in Vancouver, Canada. The research team, consisting of Rahman Mushfiqur, Mohammad Galib Shams, Nabil Mosharraf Hossain, and Riasat Islam, introduced a unique methodology that leverages large language models (LLMs) to enhance the study of Islamic historical texts.
Research Overview
The Seerah-Hadith Mapping project focuses on connecting key events from the Seerah (biography of Prophet Muhammad ﷺ) with related hadiths (authentic sayings and actions of the Prophet). By using LLMs, vector search, and semantic analysis, the project aims to automate the process of identifying relevant hadiths for specific events, making Islamic knowledge more accessible and contextually enriching for learners and researchers.
Traditional methods for studying Seerah and Hadith often require extensive manual research due to the complexity and vastness of Islamic literature. This novel approach accelerates the process by enabling AI-driven mapping that maintains contextual and theological accuracy.
Key Features of the Approach
- LLM Integration: Utilizes advanced language models to perform semantic understanding of both Seerah and Hadith texts.
- Vector Search: Embeds text in a high-dimensional vector space to efficiently retrieve semantically related hadiths for Seerah events.
Research Impact
This work is a significant advancement in the application of AI to religious studies, providing tools that streamline access to interconnected Islamic narratives. It holds potential for educational platforms, research institutions, and app developers focused on Islamic studies.
Watch and Read
Stay tuned for more updates on how this technology will shape the future of learning and research in Islamic studies.
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