MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems? Article Swipe
Renrui Zhang
,
Dongzhi Jiang
,
Yichi Zhang
,
Haokun Lin
,
Z. J. Guo
,
Pengshuo Qiu
,
Aojun Zhou
,
Pan Lu
,
Kai‐Wei Chang
,
Peng Gao
,
Hongsheng Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.14624
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.14624
The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into six distinct versions, each offering varying degrees of information content in multi-modality, contributing to 15K test samples in total. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning. In addition, we propose a Chain-of-Thought (CoT) evaluation strategy for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs. We hope the MathVerse benchmark may provide unique insights to guide the future development of MLLMs. Project page: https://mathverse-cuhk.github.io
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.14624
- https://arxiv.org/pdf/2403.14624
- OA Status
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- Cited By
- 5
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4393119387Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.14624Digital Object Identifier
- Title
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MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?Work title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-21Full publication date if available
- Authors
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Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Z. J. Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai‐Wei Chang, Peng Gao, Hongsheng LiList of authors in order
- Landing page
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https://arxiv.org/abs/2403.14624Publisher landing page
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https://arxiv.org/pdf/2403.14624Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2403.14624Direct OA link when available
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Modal, Diagram, Mathematics, Computer science, Calculus (dental), Mathematics education, Medicine, Statistics, Materials science, Orthodontics, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in-depth | 74 |
| abstract_inverted_index.insights | 201 |
| abstract_inverted_index.offering | 105 |
| abstract_inverted_index.problems | 85 |
| abstract_inverted_index.progress | 2 |
| abstract_inverted_index.publicly | 89 |
| abstract_inverted_index.sources. | 91 |
| abstract_inverted_index.strategy | 149 |
| abstract_inverted_index.superior | 16 |
| abstract_inverted_index.MathVerse | 123, 196 |
| abstract_inverted_index.addition, | 142 |
| abstract_inverted_index.analysis, | 182 |
| abstract_inverted_index.available | 90 |
| abstract_inverted_index.benchmark | 68, 197 |
| abstract_inverted_index.contexts. | 20 |
| abstract_inverted_index.diagrams. | 57 |
| abstract_inverted_index.equitable | 72 |
| abstract_inverted_index.evaluated | 30 |
| abstract_inverted_index.excessive | 39 |
| abstract_inverted_index.introduce | 62 |
| abstract_inverted_index.reasoning | 172, 189 |
| abstract_inverted_index.versions, | 103 |
| abstract_inverted_index.MathVerse, | 63 |
| abstract_inverted_index.adaptively | 169 |
| abstract_inverted_index.all-around | 65 |
| abstract_inverted_index.annotators | 99 |
| abstract_inverted_index.assessment | 153 |
| abstract_inverted_index.attention, | 12 |
| abstract_inverted_index.benchmarks | 36 |
| abstract_inverted_index.evaluation | 75, 148 |
| abstract_inverted_index.questions, | 44 |
| abstract_inverted_index.reasoning. | 140 |
| abstract_inverted_index.remarkable | 1 |
| abstract_inverted_index.understand | 134 |
| abstract_inverted_index.Multi-modal | 4 |
| abstract_inverted_index.development | 206 |
| abstract_inverted_index.incorporate | 38 |
| abstract_inverted_index.information | 109 |
| abstract_inverted_index.investigate | 34 |
| abstract_inverted_index.performance | 17 |
| abstract_inverted_index.potentially | 46 |
| abstract_inverted_index.transformed | 96 |
| abstract_inverted_index.understood. | 32 |
| abstract_inverted_index.capabilities | 23 |
| abstract_inverted_index.contributing | 113 |
| abstract_inverted_index.fine-grained | 152 |
| abstract_inverted_index.intermediate | 187 |
| abstract_inverted_index.interpreting | 54 |
| abstract_inverted_index.mathematical | 139 |
| abstract_inverted_index.meticulously | 79 |
| abstract_inverted_index.unparalleled | 11 |
| abstract_inverted_index.high-quality, | 82 |
| abstract_inverted_index.multi-subject | 83 |
| abstract_inverted_index.insufficiently | 29 |
| abstract_inverted_index.comprehensively | 125 |
| abstract_inverted_index.multi-modality, | 112 |
| abstract_inverted_index.problem-solving | 27 |
| abstract_inverted_index.Chain-of-Thought | 146 |
| abstract_inverted_index.https://mathverse-cuhk.github.io | 211 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile |