Recent advancements in Large Multimodal Models (LMMs) have shown promising
results in mathematical reasoning within visual contexts, with models
approaching human-level performance on existing benchmarks such as MathVista.
However, we observe significant limitations in the diversity of questions and
breadth of subjects covered by these benchmarks. To address this issue, we
present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of
3,040 high-quality mathematical problems with visual contexts sourced from real
math competitions. Spanning 16 distinct mathematical disciplines and graded
across 5 levels of difficulty, our dataset provides a comprehensive and diverse
set of challenges for evaluating the mathematical reasoning abilities of LMMs.
Through extensive experimentation, we unveil a notable performance gap between
current LMMs and human performance on MATH-V, underscoring the imperative for
further advancements in LMMs. Moreover, our detailed categorization allows for
a thorough error analysis of LMMs, offering valuable insights to guide future
research and development. The project is available at
this https URL

我们通过提供一组全面多样的问题来评估大规模多模态模型的数学推理能力，并发现目前的模型在 MATH-V 数据集上与人类表现存在明显差距，强调了对大规模多模态模型的进一步发展的必要性，此外，我们的详细分类还允许对其错误进行全面分析，为未来的研究和开发提供有价值的见解。