mlsanityData to 14 Oct 2026

MIDAL: A Dataset of Math Image Descriptions for Accessible Learning

arXiv 2608.00868 ↗First posted 1 Aug 2026cs.CV cs.HCcited by 0

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Abstract (arXiv)

Many open educational resources are lacking in accessibility, especially in-depth image descriptions. In subjects like Science and Mathematics, however, it can be particularly difficult to write image descriptions since there can be many complicated expressions and names depending upon the course level. To help fill that gap in a small way, we introduce Math Image Descriptions for Accessible Learning (MIDAL), a math image-description dataset of 2,020 mathematical images spanning multiple educational levels, to aid in training vision language models to create image descriptions following accessibility best practices. We hope MIDAL is a valuable resource in enhancing the conversation and innovation regarding accessibility of STEM content in higher education. This dataset is however not just limited in math description generation but can also be used to fine-tune language models that can have improved mathematical reasoning and answers.

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References · 5

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2024DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Modelscited by 146
  3. 2024From Pixels to Prose: A Large Dataset of Dense Image Captionscited by 54
  4. 2025DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Imagescited by 17
  5. 2025MMGeoLM: Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Modelscited by 2

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  4. 2024Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
  5. 2025MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts
  6. 2024MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model
  7. 2025Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs
  8. 2025Evaluating Visual Mathematics in Multimodal LLMs: A Multilingual Benchmark Based on the Kangaroo Tests