@inproceedings{elbadry-etal-2026-sahm,
    title = "{SAHM}: A Benchmark for {A}rabic Financial and Shari{'}ah-Compliant Reasoning",
    author = "Elbadry, Rania  and
      Ahmad, Sarfraz  and
      Heakl, Ahmed  and
      Bouch, Dani  and
      Ahsan, Momina  and
      AlMahri, Muhra  and
      Khalil, Marwa Elsaid  and
      Wang, Yuxia  and
      Lahlou, Salem  and
      Ananiadou, Sophia  and
      Stoyanov, Veselin  and
      Huang, Jimin  and
      Peng, Xueqing  and
      Nakov, Preslav  and
      Xie, Zhuohan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1593/",
    pages = "34509--34536",
    ISBN = "979-8-89176-390-6",
    abstract = "English financial NLP has progressed rapidly through benchmarks for sentiment, document understanding, and financial question answering, while Arabic financial NLP remains comparatively under-explored despite strong practical demand for trustworthy finance and Islamic-finance assistants. We introduce SAHM, a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari{'}ah-compliant reasoning. SAHM contains 14,380 expert-verified instances spanning seven tasks: AAOIFI standards QA, fatwa-based QA/MCQ, accounting and business exams, financial sentiment analysis, extractive summarization, and event{--}cause reasoning, curated from authentic regulatory, juristic, and corporate sources. We evaluate 19 strong open and proprietary LLMs using task-specific metrics and rubric-based scoring for open-ended outputs, and find that Arabic fluency does not reliably translate to evidence-grounded financial reasoning: models are substantially stronger on recognition-style tasks than on generation and causal reasoning, with the largest gaps on event{--}cause reasoning. We release the benchmark, evaluation framework, and an instruction-tuned model to support future research on trustworthy Arabic financial NLP."
}