SAHM：阿拉伯语金融与伊斯兰合规推理的基准
SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant Reasoning
AI总结：
本文提出SAHM基准，涵盖七个任务，包含14380个专家验证实例，评估20个LLM发现阿拉伯语流畅性不等于金融推理能力，特别是在事件因果推理上存在显著差距。
摘要：
英语金融NLP通过针对盈利分析、市场情绪、表格推理和金融问答的基准快速进步，但阿拉伯语金融NLP几乎不存在，尽管有4.22亿说话者，4.9万亿美元的海湾主权财富和4.5万亿美元的伊斯兰金融行业需要专门的Shari'ah合规性，如 sukuk、murabaha 和 takaful。我们介绍了SAHM，第一个覆盖七个任务的阿拉伯语金融基准：AAOIFI标准问答、基于fatwa的问答/单选题、会计和商业考试、金融情绪分析、提取性摘要和事件因果推理，包含14,380个专家验证的实例，来自真实监管、法学和企业来源。评估20个LLM，发现阿拉伯语流畅性不意味着金融推理能力：在识别任务中得分91%的模型在生成任务中急剧下降，事件因果推理暴露了最宽的性能差距（1.89-9.84/10）。我们发布该基准和数据集以支持可信的阿拉伯语金融助手。
英文摘要：
English financial NLP has advanced rapidly through benchmarks targeting earnings analysis, market sentiment, tabular reasoning, and financial question answering, yet Arabic financial NLP remains virtually nonexistent, despite 422 million speakers, $4.9 trillion in Gulf sovereign wealth, and a $4-5 trillion Islamic finance industry requiring specialized Shari'ah compliance over instruments like sukuk, murabaha, and takaful. We introduce Sahm, the first Arabic financial benchmark spanning seven tasks: AAOIFI standards QA, fatwa-based QA/MCQ, accounting and business exams, financial sentiment analysis, extractive summarization, and event-cause reasoning, comprising 14,380 expert-verified instances from authentic regulatory, juristic, and corporate sources. Evaluating 20 LLMs, we find Arabic fluency does not imply financial reasoning: models achieving 91% on recognition tasks drop sharply on generation, and event-cause reasoning exposes the widest performance gap (1.89-9.84/10). We release the benchmark and dataset to support trustworthy Arabic financial assistants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
