G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model Article Swipe
Jiahui Gao
,
Renjie Pi
,
Jipeng Zhang
,
Jiacheng Ye
,
Wanjun Zhong
,
Yufei Wang
,
Lanqing Hong
,
Jianhua Han
,
Hang Xu
,
Zhenguo Li
,
Lingpeng Kong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.11370
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.11370
Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mathematical problem solving. However, current work has been largely focused on text-based mathematical problems, with limited investigation in problems involving geometric information. Addressing this gap, we aim to enable LLMs to solve geometric problems by understanding image input. We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships. To overcome these challenges, we take advantage of the unique characteristics of geometric problems (such as unique geometric logical form, and geometric scalability) and the capacity of the textual LLMs to build an enriched multimodal geometry dataset based on existing data. The augmented dataset, Geo170K, contains more than 170K geometric image-caption and question-answer pairs. Utilizing our constructed Geo170K dataset, we develop G-LLaVA, which demonstrates exceptional performance in solving geometric problems, significantly outperforming GPT-4-V on the MathVista benchmark with only 7B parameters.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.11370
- https://arxiv.org/pdf/2312.11370
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389977164
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389977164Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.11370Digital Object Identifier
- Title
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G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-18Full publication date if available
- Authors
-
Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, Lingpeng KongList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.11370Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2312.11370Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2312.11370Direct OA link when available
- Concepts
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Geometric modeling, Computer science, Benchmark (surveying), Scalability, Geometric design, Image (mathematics), Artificial intelligence, Modal, Theoretical computer science, Mathematics, Geometry, Geography, Database, Polymer chemistry, Geodesy, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.their | 19, 84 |
| abstract_inverted_index.these | 88 |
| abstract_inverted_index.which | 14, 148 |
| abstract_inverted_index.(LLMs) | 3 |
| abstract_inverted_index.Models | 70 |
| abstract_inverted_index.enable | 50 |
| abstract_inverted_index.input. | 59 |
| abstract_inverted_index.models | 2 |
| abstract_inverted_index.pairs. | 139 |
| abstract_inverted_index.unique | 95, 102 |
| abstract_inverted_index.(MLLMs) | 71 |
| abstract_inverted_index.GPT-4-V | 158 |
| abstract_inverted_index.Geo170K | 143 |
| abstract_inverted_index.analyze | 62 |
| abstract_inverted_index.current | 26, 66 |
| abstract_inverted_index.dataset | 122 |
| abstract_inverted_index.develop | 146 |
| abstract_inverted_index.focused | 31 |
| abstract_inverted_index.largely | 30 |
| abstract_inverted_index.limited | 37 |
| abstract_inverted_index.logical | 104 |
| abstract_inverted_index.problem | 23 |
| abstract_inverted_index.solving | 153 |
| abstract_inverted_index.textual | 114 |
| abstract_inverted_index.G-LLaVA, | 147 |
| abstract_inverted_index.Geo170K, | 130 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.Language | 69 |
| abstract_inverted_index.capacity | 111 |
| abstract_inverted_index.contains | 131 |
| abstract_inverted_index.dataset, | 129, 144 |
| abstract_inverted_index.elements | 82 |
| abstract_inverted_index.enriched | 119 |
| abstract_inverted_index.existing | 125 |
| abstract_inverted_index.geometry | 121 |
| abstract_inverted_index.language | 1 |
| abstract_inverted_index.overcome | 87 |
| abstract_inverted_index.problems | 40, 55, 99 |
| abstract_inverted_index.research | 17 |
| abstract_inverted_index.solving. | 24 |
| abstract_inverted_index.struggle | 76 |
| abstract_inverted_index.MathVista | 161 |
| abstract_inverted_index.Utilizing | 140 |
| abstract_inverted_index.advantage | 92 |
| abstract_inverted_index.augmented | 128 |
| abstract_inverted_index.benchmark | 162 |
| abstract_inverted_index.extensive | 16 |
| abstract_inverted_index.geometric | 42, 54, 81, 98, 103, 107, 135, 154 |
| abstract_inverted_index.involving | 41 |
| abstract_inverted_index.problems, | 35, 155 |
| abstract_inverted_index.reasoning | 10 |
| abstract_inverted_index.Addressing | 44 |
| abstract_inverted_index.Multimodal | 67 |
| abstract_inverted_index.accurately | 78 |
| abstract_inverted_index.encourages | 15 |
| abstract_inverted_index.generation | 12 |
| abstract_inverted_index.multimodal | 120 |
| abstract_inverted_index.remarkable | 6 |
| abstract_inverted_index.text-based | 33 |
| abstract_inverted_index.application | 20 |
| abstract_inverted_index.challenges, | 89 |
| abstract_inverted_index.constructed | 142 |
| abstract_inverted_index.exceptional | 150 |
| abstract_inverted_index.human-level | 9 |
| abstract_inverted_index.limitations | 64 |
| abstract_inverted_index.parameters. | 166 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.proficiency | 7 |
| abstract_inverted_index.demonstrates | 149 |
| abstract_inverted_index.information. | 43 |
| abstract_inverted_index.mathematical | 22, 34 |
| abstract_inverted_index.scalability) | 108 |
| abstract_inverted_index.capabilities, | 13 |
| abstract_inverted_index.comprehending | 79 |
| abstract_inverted_index.image-caption | 136 |
| abstract_inverted_index.investigation | 38 |
| abstract_inverted_index.outperforming | 157 |
| abstract_inverted_index.significantly | 156 |
| abstract_inverted_index.understanding | 57 |
| abstract_inverted_index.relationships. | 85 |
| abstract_inverted_index.characteristics | 96 |
| abstract_inverted_index.question-answer | 138 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |