# Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation

arXiv: https://arxiv.org/abs/2509.01081 · HTML: https://mlsanity.com/paper/2509.01081/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 9 of 9)

- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://mlsanity.com/paper/2308.16149/) (2023)
- [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)
- [ArabianGPT: Native Arabic GPT-based Large Language Model](https://mlsanity.com/paper/2402.15313/) (2024)
- [Improving LLM Reliability with RAG in Religious Question-Answering: MufassirQAS](https://mlsanity.com/paper/2401.15378/) (2024)
- [A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text](https://mlsanity.com/paper/2409.09844/) (2024)
- [From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents](https://mlsanity.com/paper/2506.15911/) (2025)

## Built on by (most cited first, 10 of 10)

- [From RAG to Agentic RAG for Faithful Islamic Question Answering](https://mlsanity.com/paper/2601.07528/) (2026)
- [MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs](https://mlsanity.com/paper/2603.07539/) (2026)
- [QIAS 2026: Overview of the Shared Task on Islamic Inheritance Reasoning](https://mlsanity.com/paper/2606.13756/) (2026)
- [HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering](https://mlsanity.com/paper/2607.20219/) (2026)
- [HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification](https://mlsanity.com/paper/2608.03966/) (2026)
- [Transformer Tafsir at QIAS 2025 Shared Task: Hybrid Retrieval-Augmented Generation for Islamic Knowledge Question Answering](https://mlsanity.com/paper/2509.23793/) (2025)
- [SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models](https://mlsanity.com/paper/2603.12768/) (2026)
- [Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI](https://mlsanity.com/paper/2606.16629/) (2026)
- [Detecting Hallucinations and Recovering Verified Answers in Arabic Islamic Question Answering](https://mlsanity.com/paper/2608.03720/) (2026)
- [Halluscoring 2026: The first shared task on llms hallucination detection and answer verification](https://mlsanity.com/paper/2609.38355/) (2026)

## Abstract (arXiv)

This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, known as 'ilm al-mawarith. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering diverse inheritance scenarios, designed to test models' ability to understand the inheritance context and compute the distribution of shares prescribed by Islamic jurisprudence. The results reveal a significant performance gap: o3 and Gemini 2.5 achieved accuracies above 90%, whereas ALLaM, Fanar, LLaMA, and Mistral scored below 50%. These disparities reflect important differences in reasoning ability and domain adaptation. We conduct a detailed error analysis to identify recurring failure patterns across models, including misunderstandings of inheritance scenarios, incorrect application of legal rules, and insufficient domain knowledge. Our findings highlight limitations in handling structured legal reasoning and suggest directions for improving performance in Islamic legal reasoning. Code: https://github.com/bouchekif/inheritance_evaluation