@inproceedings{kurdi-etal-2026-silah,
title = "Silah at {QIAS} 2026: Fine-Tuning vs. Retrieval-Augmented Generation for Islamic Inheritance Reasoning",
author = "Kurdi, Ghader and
Justanieah, Hanan and
Justanieah, Hala",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.27/",
doi = "10.63317/4iyrxakdovsm",
pages = "213--219",
abstract = "Islamic inheritance is a highly structured and rule-intensive domain that requires precise reasoning. The QIAS 2026 Shared Task introduces a benchmark for evaluating generative artificial intelligence on end-to-end inheritance problem solving. In this paper, we present our team Silah{'}s participation in the QIAS 2026 shared task, where we compare three approaches: (1) a multi-stage retrieval-augmented, rule-guided pipeline, (2) supervised fine-tuning of generative large language models, and (3) a retrieval-augmented fine-tuning approach. We evaluate several open-source models, including Qwen2.5, Llama, DeepSeek, and Fanar. Our results show that supervised fine-tuning consistently outperforms retrieval-based approaches, with the fine-tuned Fanar-1-9B-Instruct model achieving the best performance (MIR-E = 0.83) and ranking sixth overall in the shared task. These findings suggest that learning implicit reasoning patterns through fine-tuning is more effective than explicit rule injection under current retrieval setups, and emphasize the need for more accurate and minimal rule selection mechanisms in future retrieval-augmented approaches."
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<abstract>Islamic inheritance is a highly structured and rule-intensive domain that requires precise reasoning. The QIAS 2026 Shared Task introduces a benchmark for evaluating generative artificial intelligence on end-to-end inheritance problem solving. In this paper, we present our team Silah’s participation in the QIAS 2026 shared task, where we compare three approaches: (1) a multi-stage retrieval-augmented, rule-guided pipeline, (2) supervised fine-tuning of generative large language models, and (3) a retrieval-augmented fine-tuning approach. We evaluate several open-source models, including Qwen2.5, Llama, DeepSeek, and Fanar. Our results show that supervised fine-tuning consistently outperforms retrieval-based approaches, with the fine-tuned Fanar-1-9B-Instruct model achieving the best performance (MIR-E = 0.83) and ranking sixth overall in the shared task. These findings suggest that learning implicit reasoning patterns through fine-tuning is more effective than explicit rule injection under current retrieval setups, and emphasize the need for more accurate and minimal rule selection mechanisms in future retrieval-augmented approaches.</abstract>
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%0 Conference Proceedings
%T Silah at QIAS 2026: Fine-Tuning vs. Retrieval-Augmented Generation for Islamic Inheritance Reasoning
%A Kurdi, Ghader
%A Justanieah, Hanan
%A Justanieah, Hala
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F kurdi-etal-2026-silah
%X Islamic inheritance is a highly structured and rule-intensive domain that requires precise reasoning. The QIAS 2026 Shared Task introduces a benchmark for evaluating generative artificial intelligence on end-to-end inheritance problem solving. In this paper, we present our team Silah’s participation in the QIAS 2026 shared task, where we compare three approaches: (1) a multi-stage retrieval-augmented, rule-guided pipeline, (2) supervised fine-tuning of generative large language models, and (3) a retrieval-augmented fine-tuning approach. We evaluate several open-source models, including Qwen2.5, Llama, DeepSeek, and Fanar. Our results show that supervised fine-tuning consistently outperforms retrieval-based approaches, with the fine-tuned Fanar-1-9B-Instruct model achieving the best performance (MIR-E = 0.83) and ranking sixth overall in the shared task. These findings suggest that learning implicit reasoning patterns through fine-tuning is more effective than explicit rule injection under current retrieval setups, and emphasize the need for more accurate and minimal rule selection mechanisms in future retrieval-augmented approaches.
%R 10.63317/4iyrxakdovsm
%U https://aclanthology.org/2026.osact-1.27/
%U https://doi.org/10.63317/4iyrxakdovsm
%P 213-219
Markdown (Informal)
[Silah at QIAS 2026: Fine-Tuning vs. Retrieval-Augmented Generation for Islamic Inheritance Reasoning](https://aclanthology.org/2026.osact-1.27/) (Kurdi et al., OSACT 2026)
ACL