首个大规模波兰语大模型评测基准，覆盖近1.9万道试题
LLMzSzŁ: a comprehensive LLM benchmark for Polish
- 基于波兰国家考试题库构建，涵盖154个领域、4类考试
- 多语言模型在波兰语任务上表现优于单语模型，但小模型更省资源
- 可用于检测考题异常，适合教育AI与语言模型评估研究者
本文提出了首个大规模波兰语大模型评测基准LLMzSzŁ（LLMs Behind the School Desk），基于波兰中央考试委员会档案中的全国性考试题库。该基准包含154个领域、4类考试，共约1.9万道封闭式题目。我们评估了开源多语言、英语及波兰语大模型的性能，验证其跨语言知识迁移能力。同时分析了模型与人类在准确率和通过率上的相关性。结果表明，多语言模型在波兰语任务中表现更优；但在模型规模受限时，单语模型更具优势。研究揭示了大模型在辅助考题验证方面的潜力，尤其在识别题目异常或错误方面。
原文摘要 · Abstract (English)
This article introduces the first comprehensive benchmark for the Polish language at this scale: LLMzSzŁ (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.
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