Exploring the Utility of Large Language Models in Improving the Precision of Narrator Name Extraction

Kaymaram, Shawn Nassabi, Ibrahim Akar

Keywords: Computational Hadith Analysis, Large Language Models, Sanad Analysis, AI applications in digital humanities

TL;DR: This paper presents a large language model–based framework that significantly improves the accuracy of hadith narrator name extraction by combining advanced NLP techniques with domain-specific knowledge.

Abstract: The precise extraction of hadith narrator names is a critical task in computational hadith scholarship. This paper explores the utility of Large Language Models (LLMs) in automating and enhancing the process of hadith narrator name extraction. We present an analysis of existing methodologies, highlighting their limitations in handling the complexities of Arabic language nuances and the intricate relationships between narrators. By leveraging the contextual understanding and generative capabilities of LLMs, we propose a novel framework that integrates advanced natural language processing techniques with domain-specific knowledge. Our experiments demonstrate improvements in accuracy and efficiency compared to traditional machine learning methods.

Track: Track 1: ML on Islamic Content / ML for Muslim Communities

Submission Number: 6