用轻量编码器实现伊斯兰继承计算，兼顾效率与隐私。
CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning
- 采用专用阿拉伯语编码器+注意力相关性评分，不依赖生成式推理。
- 基于MARBERT的模型达69.87%准确率，支持本地部署。
- 适合对实时性、隐私要求高的法律推理场景。
伊斯兰继承法（Ilm al-Mawarith）要求精确识别继承人并计算份额，对AI构成挑战。本文提出一种轻量级框架，使用专用阿拉伯语文本编码器与注意力相关性评分（ARS）解决多选题继承问题。系统根据语义相关性对选项排序，实现无需生成推理的快速本地推断。在QIAS 2025数据集上对比了MARBERT、ArabicBERT、AraBERT等编码器与Gemini、DeepSeek等API大模型。尽管大模型最高达87.6%准确率，但资源消耗高且依赖上下文；我们的MARBERT方案达69.87%，展现出高效、可本地部署及隐私保护优势。本工作量化了大模型峰值性能与小模型实用价值之间的权衡，在高风险领域具有重要意义。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。