TCE at IslamicEval 2025: Retrieval-Augmented LLMs for Quranic and Hadith Content Identification and Verification
Year: 2025
Type: conference-paper
Abstract: Recent advancements in large language models (LLMs) have opened new possibilities for processing complex natural language tasks, including those involving highly regarded religious content.However, working with divine sources such as the Holy Quran and Hadith presents unique challenges.These Classical Arabic texts have, for centuries, been meticulously preserved and recited word-for-word, allowing no tolerance for errors -even a single incorrect diacritic can entirely alter the meaning.Such sensitivity demands exceptional precision, as hallucinations or inaccuracies from LLMs could lead to significant misinterpretations among general users.To address this challenge, we present an Arabic-focused, LLM-powered framework designed to identify and verify the integrity of religious text generated by widely used LLMs.Evaluation on benchmark subtasks demonstrates strong performance, achieving a Macro-Avg F1 score of 86.11% on Subtask 1A and an Accuracy of 89.82% on Subtask 1B.
Institutions: Rensselaer Polytechnic Institute, Tanta University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037755