@inproceedings{shairah-etal-2025-alarb,
title = "{ALARB}: An {A}rabic Legal Argument Reasoning Benchmark",
author = "Shairah, Harethah Abu and
Alharbi, Somayah S. and
AlHussein, Abdulaziz A. and
Alsabea, Sameer and
Shaqaqi, Omar and
Alshamlan, Hebah A. and
Knio, Omar and
Turkiyyah, George",
editor = "Darwish, Kareem and
Ali, Ahmed and
Abu Farha, Ibrahim and
Touileb, Samia and
Zitouni, Imed and
Abdelali, Ahmed and
Al-Ghamdi, Sharefah and
Alkhereyf, Sakhar and
Zaghouani, Wajdi and
Khalifa, Salam and
AlKhamissi, Badr and
Almatham, Rawan and
Hamed, Injy and
Alyafeai, Zaid and
Alowisheq, Areeb and
Inoue, Go and
Mrini, Khalil and
Alshammari, Waad",
booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.arabicnlp-main.32/",
pages = "389--406",
ISBN = "979-8-89176-352-4",
abstract = "We introduce ALARB, a dataset and suite of tasks designed to evaluate the reasoning capabilities of large language models (LLMs) within the Arabic legal domain. While existing Arabic benchmarks cover some knowledge-intensive tasks such as retrieval and understanding, substantial datasets focusing specifically on multistep reasoning for Arabic LLMs, especially in open-ended contexts, are lacking. The dataset comprises over 13K commercial court cases from Saudi Arabia, with each case including the facts presented, the reasoning of the court, the verdict, as well the cited clauses extracted from the regulatory documents. We define a set of challenging tasks leveraging this dataset and reflecting the complexity of real-world legal reasoning, including verdict prediction, completion of reasoning chains in multistep legal arguments, and identification of relevant regulations based on case facts. We benchmark a representative selection of current open and closed Arabic LLMs on these tasks and demonstrate the dataset{'}s utility for instruction tuning. Notably, we show that instruction tuning a modest 12B parameter model using ALARB significantly enhances its performance in verdict prediction and Arabic verdict generation, reaching a level comparable to that of GPT-4o."
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%0 Conference Proceedings
%T ALARB: An Arabic Legal Argument Reasoning Benchmark
%A Shairah, Harethah Abu
%A Alharbi, Somayah S.
%A AlHussein, Abdulaziz A.
%A Alsabea, Sameer
%A Shaqaqi, Omar
%A Alshamlan, Hebah A.
%A Knio, Omar
%A Turkiyyah, George
%Y Darwish, Kareem
%Y Ali, Ahmed
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%Y Zitouni, Imed
%Y Abdelali, Ahmed
%Y Al-Ghamdi, Sharefah
%Y Alkhereyf, Sakhar
%Y Zaghouani, Wajdi
%Y Khalifa, Salam
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Hamed, Injy
%Y Alyafeai, Zaid
%Y Alowisheq, Areeb
%Y Inoue, Go
%Y Mrini, Khalil
%Y Alshammari, Waad
%S Proceedings of The Third Arabic Natural Language Processing Conference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-352-4
%F shairah-etal-2025-alarb
%X We introduce ALARB, a dataset and suite of tasks designed to evaluate the reasoning capabilities of large language models (LLMs) within the Arabic legal domain. While existing Arabic benchmarks cover some knowledge-intensive tasks such as retrieval and understanding, substantial datasets focusing specifically on multistep reasoning for Arabic LLMs, especially in open-ended contexts, are lacking. The dataset comprises over 13K commercial court cases from Saudi Arabia, with each case including the facts presented, the reasoning of the court, the verdict, as well the cited clauses extracted from the regulatory documents. We define a set of challenging tasks leveraging this dataset and reflecting the complexity of real-world legal reasoning, including verdict prediction, completion of reasoning chains in multistep legal arguments, and identification of relevant regulations based on case facts. We benchmark a representative selection of current open and closed Arabic LLMs on these tasks and demonstrate the dataset’s utility for instruction tuning. Notably, we show that instruction tuning a modest 12B parameter model using ALARB significantly enhances its performance in verdict prediction and Arabic verdict generation, reaching a level comparable to that of GPT-4o.
%U https://aclanthology.org/2025.arabicnlp-main.32/
%P 389-406
Markdown (Informal)
[ALARB: An Arabic Legal Argument Reasoning Benchmark](https://aclanthology.org/2025.arabicnlp-main.32/) (Shairah et al., ArabicNLP 2025)
ACL
- Harethah Abu Shairah, Somayah S. Alharbi, Abdulaziz A. AlHussein, Sameer Alsabea, Omar Shaqaqi, Hebah A. Alshamlan, Omar Knio, and George Turkiyyah. 2025. ALARB: An Arabic Legal Argument Reasoning Benchmark. In Proceedings of The Third Arabic Natural Language Processing Conference, pages 389–406, Suzhou, China. Association for Computational Linguistics.