VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM

• URL: http://arxiv.org/abs/2411.05423v1
• Date: Fri, 08 Nov 2024 09:15:56 GMT
• Title: VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM
• Authors: Jeongwoo Lee, Kwangsuk Park, Jihyeon Park,
• Abstract summary: This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem's core mathematical concepts, improving both problem creation and assessment.
• Score: 0.5383910843560784
• License:
• Abstract: Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem's core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks such as numeric calculation, geometry validation, and visualization, our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms basic LLMs in terms of text coherence, consistency, relevance and similarity, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.

Related papers

• Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding [94.64781599202882]
  Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks. They often struggle with visual arithmetic, seemingly simple capabilities like object counting or length comparison. We propose CogAlign, a novel post-training strategy inspired by Piaget's theory of cognitive development.
  arXiv  Detail & Related papers  (2025-02-17T06:54:49Z)
• Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization [56.17811386955609]
  Graph-structured challenges are inherently difficult due to their nonlinear and intricate nature. In this study, we propose transforming graphs into images to preserve their higher-order structural features accurately. By combining the innovative paradigm powered by multimodal large language models with simple search techniques, we aim to develop a novel and effective framework.
  arXiv  Detail & Related papers  (2025-01-21T08:28:10Z)
• Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs [62.875934732547435]
  Current large language models (MLLMs) often underperform on mathematical problem-solving tasks that require fine-grained visual understanding. In this paper, we evaluate the visual grounding capabilities of state-of-the-art MLLMs and reveal a significant negative correlation between visual grounding accuracy and problem-solving performance. We propose a novel approach, SVE-Math, featuring a geometric-grounded vision encoder and a feature router that dynamically adjusts the contribution of hierarchical visual feature maps.
  arXiv  Detail & Related papers  (2025-01-11T04:08:44Z)
• VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning [47.81337826098964]
  We present VisAidMath, a benchmark for evaluating the problem-solving process related to visual information. This benchmark includes 1,200 challenging problems from various mathematical branches, vision-aid formulations, and difficulty levels. We conduct evaluations on ten mainstream LLMs and LMMs, highlighting deficiencies in the visual-aided reasoning process.
  arXiv  Detail & Related papers  (2024-10-30T13:19:44Z)
• Diagram Formalization Enhanced Multi-Modal Geometry Problem Solver [11.69164802295844]
  We introduce a new framework that integrates visual features, geometric formal language, and natural language representations. We propose a novel synthetic data approach and create a large-scale geometric dataset, SynthGeo228K, annotated with both formal and natural language captions. Our framework improves MLLMs' ability to process geometric diagrams and extends their application to open-ended tasks on the formalgeo7k dataset.
  arXiv  Detail & Related papers  (2024-09-06T12:11:06Z)
• Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning [5.9767694994869425]
  Multimodal Large Language Models (MLLMs) excel in solving text-based mathematical problems. They struggle with mathematical diagrams since they are primarily trained on natural scene images. We propose Math-PUMA, a methodology focused on Progressive Upward Multimodal Alignment.
  arXiv  Detail & Related papers  (2024-08-16T10:11:05Z)
• Multi-qubit state visualizations to support problem solving $-$ a pilot study [1.8879980022743639]
  We compare students' performance, time taken, and cognitive load when solving problems using the mathematical-symbolic Dirac notation alone with using it accompanied by the circle notation or the dimensional circle notation in single- and multi-qubit systems. Although little overall differences in students' performance can be detected depending on the presented representations, we observe that problem-solving performance is student- and context-dependent.
  arXiv  Detail & Related papers  (2024-06-24T11:46:35Z)
• G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model [124.68242155098189]
  Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities. G-LLaVA demonstrates exceptional performance in solving geometric problems, significantly outperforming GPT-4-V on the MathVista benchmark with only 7B parameters.
  arXiv  Detail & Related papers  (2023-12-18T17:36:20Z)
• Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning [95.18337034090648]
  We propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG) These visual arithmetic problems are in the form of geometric figures. We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task.
  arXiv  Detail & Related papers  (2020-04-25T17:14:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.