数学奥德赛:使用奥德赛数学数据在大型语言模型中对数学解决问题的技能进行基准测试
Meng Fang1, Xiangpeng Wan2, Fei Lu3
1Department of Computer Science, University of Liverpool, Liverpool, UK. Meng.Fang@liverpool.ac.uk.
Scientific data
|August 8, 2025
概括
创建了一个新的数据集,MathOdyssey,用于评估数学推理的大型语言模型 (LLM). 本资源有助于评估和改进对复杂数学问题的LLM绩效.
科学领域:
- 人工智能的人工智能
- 数学教育教育 数学教育
背景情况:
- 大型语言模型 (LLM) 擅长自然语言任务,但难以处理复杂的数学推理.
- 评估LLM数学能力需要专门的数据集进行严格的评估.
研究的目的:
- 介绍MathOdyssey,这是一个用于评估LLMs数学推理的新型数据集.
- 提供一个标准化资源,用于可重现的数学LLM绩效评估.
主要方法:
- 策划了387个由专家生成的数学问题,从高中到奥林匹克水平.
- 包括详细的解决方案和按难度,主题和答案类型分类的问题.
- 通过专家贡献,同行评审和标准化格式化开发数据集.
主要成果:
- 在MathOdyssey数据集上评估了代表性LLM的表现.
- 在各种问题类型和难度级别上报告了LLM的表现.
结论:
- MathOdyssey作为一个开放访问资源,用于对LLM数学能力的细粒度评估.
- 该数据集将促进人工智能驱动的数学推理和教育方面的研究.
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