标题： LLMzSzŁ：波兰语的全面LLM基准 显示英文标题

标题： LLMzSzŁ: a comprehensive LLM benchmark for Polish

摘要： 本文介绍了针对波兰语的首个全面基准测试：LLMzSz{\L } (LLMs Behind the School Desk)。 它是基于一系列连贯的波兰国家考试，包括从波兰中央考试委员会档案中提取的学术和专业考试。 它涵盖4种类型的考试，来自154个领域。 总共包含近19000道封闭式问题。 我们研究了开源多语言、英语和波兰语LLMs的性能，以验证LLMs在不同语言之间迁移知识的能力。 同时，还考察了模型准确率和考试通过率层面的LLMs与人类之间的相关性。 我们表明，多语言LLMs可以在单语言LLMs之上获得更好的结果；然而，当模型大小重要时，单语言模型可能更有益。 我们的分析突显了LLMs在辅助考试验证方面的潜力，特别是在识别考试任务中的异常或错误方面。 显示英文摘要

摘要： This article introduces the first comprehensive benchmark for the Polish language at this scale: LLMzSz{\L} (LLMs Behind the School Desk). It is based on a coherent collection of Polish national exams, including both academic and professional tests extracted from the archives of the Polish Central Examination Board. It covers 4 types of exams, coming from 154 domains. Altogether, it consists of almost 19k closed-ended questions. We investigate the performance of open-source multilingual, English, and Polish LLMs to verify LLMs' abilities to transfer knowledge between languages. Also, the correlation between LLMs and humans at model accuracy and exam pass rate levels is examined. We show that multilingual LLMs can obtain superior results over monolingual ones; however, monolingual models may be beneficial when model size matters. Our analysis highlights the potential of LLMs in assisting with exam validation, particularly in identifying anomalies or errors in examination tasks.