Year: 2025
Type: conference-paper
Abstract: In this paper, we present a system for solving Islamic inheritance problems using large language models (LLMs), focusing on accurate reasoning in Arabic based on fara'id rules.Our approach is built on the Qwen3-4B model, quantized, and trained using the Unsloth framework for efficiency.We explore multiple training strategies: (1) retrieval-augmented generation (RAG) using fatwas from Islamweb, (2) supervised fine-tuning (SFT) on annotated inheritance datasets, (3) instruction tuning of a base Qwen model followed by GRPO training for multiple choice question solving, and (4) a two-stage pipeline involving SFT on a classical Islamic inheritance book followed by MCQ fine-tuning.Among these, the fourth approach achieved 97.2% accuracy, outperforming all other submissions and ranking our team first in the competition.
Authors: Eman Elrefai, Mohamed Lotfy Elrefai, Aml Hassan Esmail
Institutions: Alexandria University, Ain Shams University, Benha University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037576