AGS-KSU at QIAS 2026: A Comparative Study of Prompting and LLM Approaches for Structured Islamic Inheritance Reasoning
Year: 2026
Type: conference-paper
Abstract: This paper describes our submission to the QIAS 2026 shared task on structured Islamic inheritance reasoning, based on the MAWARITH benchmark (Bouchekif et al., 2026).The task requires multi-step structured prediction for Arabic inheritance cases, including heir identification, blocking, share assignment, adjustment detection, and final distribution, evaluated with the MIR-E metric.We compare four system configurations: a QLoRA fine-tuned Qwen2.5-3Bbaseline, a multi-stage Fanar-Sadiq pipeline with deterministic validation and post-processing, and two GPT-5.4prompting setups.On the official test set, the best result was achieved by GPT-5.4 with explicit inheritance rules and development examples used as in-context demonstrations, reaching a MIR-E score of 0.84, compared with 0.76 for a minimal-prompt GPT-5.4 variant.These results suggest that explicit rule conditioning and in-context demonstrations can improve performance in this setup.Since the compared systems vary in model family and prompting strategy, the findings should be interpreted as a comparison of task configurations rather than a controlled model-only comparison.
Author: Hicham Ghazi Sidaoui
Institution: King Saud University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166591913