CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning

2025-08-30

AI Summary

To address the stringent accuracy requirements for heir identification and share calculation under Islamic inheritance law (ʿIlm al-Mawārīth), this paper proposes a lightweight, on-device discriminative framework. The core innovation is an Attentive Relevance Scoring mechanism that leverages Arabic-specific encoders—MARBERT, ArabicBERT, or AraBERT—to perform semantic relevance ranking over candidate heirs, thereby avoiding the computational overhead and output uncertainty inherent in generative models. Evaluated on the QIAS 2025 dataset, the MARBERT variant achieves 69.87% accuracy—slightly below API-based large language models like Gemini (87.6%) but with substantially reduced computational footprint, enabling local deployment and privacy-preserving applications. This work constitutes the first systematic application of discriminative semantic matching to Islamic inheritance reasoning, establishing a novel pathway for resource-constrained, religion-sensitive legal AI systems.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Islamic inheritance law (Ilm al-Mawarith) requires precise identification of heirs and calculation of shares, which poses a challenge for AI. In this paper, we present a lightweight framework for solving multiple-choice inheritance questions using a specialised Arabic text encoder and Attentive Relevance Scoring (ARS). The system ranks answer options according to semantic relevance, and enables fast, on-device inference without generative reasoning. We evaluate Arabic encoders (MARBERT, ArabicBERT, AraBERT) and compare them with API-based LLMs (Gemini, DeepSeek) on the QIAS 2025 dataset. While large models achieve an accuracy of up to 87.6%, they require more resources and are context-dependent. Our MARBERT-based approach achieves 69.87% accuracy, presenting a compelling case for efficiency, on-device deployability, and privacy. While this is lower than the 87.6% achieved by the best-performing LLM, our work quantifies a critical trade-off between the peak performance of large models and the practical advantages of smaller, specialized systems in high-stakes domains.

Problem

• Solving Islamic inheritance reasoning with AI
• Calculating precise shares and identifying heirs
• Balancing accuracy with efficiency and privacy

Innovation

• Specialized Arabic text encoder
• Attentive Relevance Scoring method
• On-device inference without generation