GAUSS：大型语言模型结构化数学技能基准测试
GAUSS: Benchmarking Structured Mathematical Skills for Large Language Models
AI总结：
本文提出GAUSS基准，从十二个核心技能维度评估大语言模型的数学能力，并通过GPT-5-thinking与o4-mini-high的对比展示其价值。
摘要：
我们推出了GAUSS（数学中潜在结构化技能的一般评估），这是一个基准测试，用于评估大型语言模型在十二个核心技能维度上的数学能力，这些维度分为三个领域：知识与理解、问题解决与沟通、以及元技能与创造力。通过根据认知技能对问题进行分类，并设计隔离特定能力的任务，GAUSS构建了模型数学能力的全面、细粒度且可解释的画像。这些画像忠实地代表了它们潜在的数学智能。为了举例说明如何使用GAUSS基准，我们推导了GPT-5-thinking的技能画像，揭示了其优势与劣势，以及相对于o4-mini-high的差异，从而强调了基于多维技能评估的价值。
英文摘要：
We introduce \textbf{GAUSS} (\textbf{G}eneral \textbf{A}ssessment of \textbf{U}nderlying \textbf{S}tructured \textbf{S}kills in Mathematics), a benchmark that evaluates LLMs' mathematical abilities across twelve core skill dimensions, grouped into three domains: knowledge and understanding, problem solving and communication, and meta-skills and creativity. By categorizing problems according to cognitive skills and designing tasks that isolate specific abilities, GAUSS constructs comprehensive, fine-grained, and interpretable profiles of models' mathematical abilities. These profiles faithfully represent their underlying mathematical intelligence. To exemplify how to use the \textsc{GAUSS} benchmark, we have derived the skill profile of \textsc{GPT-5-thinking}, revealing its strengths and weaknesses as well as its differences relative to \textsc{o4-mini-high}, thereby underscoring the value of multidimensional, skill-based evaluation.
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。
