# Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases

arXiv: https://arxiv.org/abs/2508.15796 · HTML: https://mlsanity.com/paper/2508.15796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles](https://mlsanity.com/paper/2504.03520/) (2025)
- [Building Domain-Specific LLMs Faithful To The Islamic Worldview: Mirage or Technical Possibility?](https://mlsanity.com/paper/2312.06652/) (2023)
- [A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles](https://mlsanity.com/paper/2404.04261/) (2024)

## Built on by (most cited first, 1 of 1)

- [IslamicLegalBench: Evaluating LLMs Knowledge and Reasoning of Islamic Law Across 1,200 Years of Islamic Pluralist Legal Traditions](https://mlsanity.com/paper/2602.21226/) (2026)

## Abstract (arXiv)

Islamic inheritance domain holds significant importance for Muslims to ensure fair distribution of shares between heirs. Manual calculation of shares under numerous scenarios is complex, time-consuming, and error-prone. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to assist with complex legal reasoning tasks. This study evaluates the reasoning capabilities of state-of-the-art LLMs to interpret and apply Islamic inheritance laws. We utilized the dataset proposed in the ArabicNLP QIAS 2025 challenge, which includes inheritance case scenarios given in Arabic and derived from Islamic legal sources. Various base and fine-tuned models, are assessed on their ability to accurately identify heirs, compute shares, and justify their reasoning in alignment with Islamic legal principles. Our analysis reveals that the proposed majority voting solution, leveraging three base models (Gemini Flash 2.5, Gemini Pro 2.5, and GPT o3), outperforms all other models that we utilized across every difficulty level. It achieves up to 92.7% accuracy and secures the third place overall in Task 1 of the Qias 2025 challenge.