评估哈萨克斯坦大学入学数学考试的法学士学位
Shirali Kadyrov1,2, Bolatbek Abdrasilov3, Aslan Sabyrov3
1Narxoz University, Almaty, Kazakhstan.
Frontiers in artificial intelligence
|October 1, 2025
概括
大型语言模型 (LLM) 在俄罗斯数学测试中表现强,挑战了关于非英语语言能力的假设. 先进的技术显著提高了准确性,突出了LLM在公平教育获取方面的潜力.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 教育技术的教育技术
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于教育应用,包括高风险的标准化测试.
- 现有的数学推理的LLM基准主要集中在英语上,引发了对资源不足的语言表现的担忧.
- 哈萨克斯坦统一的国家测试 (UNT) 数学组件对于大学入学至关重要.
研究的目的:
- 评价来自UNT的俄罗斯语言数学问题的六个LLM的表现.
- 在非英语,资源不足的教育环境中评估LLM能力.
- 调查不同评估条件对LLM准确性的影响.
主要方法:
- 六个LLM (克劳德,DeepSeek,双子座,拉玛,Qwen,o1) 在139个UNT多选数学题中进行了测试.
- 在三个条件下进行了评估:零射击,与SymPy的混合集成,以及多代理精炼框架.
- 数学课题包括代数,函数,几何,不等式和三角学.
主要成果:
- 在零射击设置中,DeepSeek,Gemini,Qwen和o1实现了91.2-100%的准确性.
- 克劳德和拉玛的初始准确度较低 (43.5-76.5%),但在混合 (高达+39.9%) 和多剂 (高达+58.1%) 方法中显著改善.
- 多剂精炼条件使克劳德能够达到97.8%的精度.
结论:
- 与此之前的假设相反,LLM在非英语语言的数学任务中表现出竞争力.
- 这些发现支持了LLM在增强双语教育和促进平等获得高等教育方面的潜力.
- 先进的LLM评估技术,如多代理精细化,可以在具有挑战性的语言环境中显著提高性能.
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