Summary of Improving Multimodal Llms Ability in Geometry Problem Solving, Reasoning, and Multistep Scoring, by Avinash Anand et al.
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring
by Avinash Anand, Raj Jaiswal, Abhishek Dharmadhikari, Atharva Marathe, Harsh Parimal Popat, Harshil Mital, Kritarth Prasad, Rajiv Ratn Shah, Roger Zimmermann
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces GPSM4K, a large-scale dataset designed to enhance the problem-solving abilities of Large Vision Language Models (LVLMs). The dataset comprises 2157 multimodal question-answer pairs extracted from mathematics textbooks for grades 7-12 and is expanded to 5340 problems, including numerical and theorem-proving questions. Unlike existing datasets like PGPS9k, Geometry3K, and Geo170K, which focus on objective-type questions, GPSM4K provides detailed step-by-step solutions, making it an excellent benchmark for assessing LVLMs’ geometric reasoning capabilities. The authors demonstrate that open-source language models require improvement in geometry problem-solving, but finetuning on the training set enhances model performance. Additionally, they evaluate the effectiveness of techniques like image captioning and Retrieval Augmentation generation (RAG) on model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big dataset called GPSM4K to help computers understand math problems better. The dataset has lots of questions and answers that come from math textbooks for students in grades 7-12. It’s different from other datasets because it includes step-by-step solutions, which helps us see how well the computer is doing. The researchers found that current language models need some work to solve math problems correctly. But if they learn from this new dataset, they can get better at solving math questions. |
Keywords
» Artificial intelligence » Image captioning » Rag
