QIAS 2026: Overview of the Shared Task on Islamic Inheritance Reasoning
Quick Answer
This paper shows that The QIAS 2026 shared task evaluates large language models' reasoning in Islamic inheritance, utilizing the MAWARITH dataset of 12,500 annotated cases.
Quick Take
Sixteen teams participated, revealing significant challenges in legal interpretation and numerical reasoning, with results indicating current models struggle with complex inheritance calculations.
Key Points
- QIAS 2026 is part of the OSACT7 Workshop at LREC 2026.
- The MAWARITH dataset includes 12,500 Arabic inheritance cases.
- Evaluation used MIR-E, measuring performance across inheritance reasoning stages.
- Sixteen teams explored various approaches, including prompting and fine-tuning.
- Current models struggle with precise legal interpretation and numerical reasoning.
Paper Resources
Source Excerpt
arXiv:2606. 13756v1 Announce Type: new Abstract: This paper presents a comprehensive overview of the QIAS 2026 shared task, organized as part of the OSACT7 Workshop and co-located with LREC 2026. The shared task was designed to evaluate the ability of to perform complex reasoning in the religious and legal domain of Islamic inheritance.
Unlike conventional question-answering benchmarks, QIAS 2026 focuses on end-to-end reasoning from natural language cases, requiring systems to perform the full inheritance calculation process, from identifying the eligible heirs to assigning the correct share to each beneficiary. …
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →TriAgent: Divergence-Aware Committees for Cost-Efficient Financial Sentiment Analysis
TriAgent introduces a cost-efficient multi-agent system for financial sentiment analysis, combining VADER, FinBERT, and Qwen2.5. It achieves an F1 score of ~0.87 with significant savings of $9.3M/year at a 10M-user scale compared to GPT-4o-mini, while also detecting hallucinations with an AUC of 0.90.