VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
- URL: http://arxiv.org/abs/2405.04950v1
- Date: Wed, 8 May 2024 10:42:48 GMT
- Title: VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
- Authors: Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, Min Zhang,
- Abstract summary: We design a benchmark named VisionGraph, used to explore the capabilities of advanced LMMs in solving multimodal graph theory problems.
We present a Description-Program-Reasoning (DPR) chain to enhance the logical accuracy of reasoning processes.
Our study shows that GPT-4V outperforms Gemini Pro in multi-step graph reasoning.
- Score: 41.11701706312843
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual context. Yet, a challenging type of visual math lies in the multimodal graph theory problem, which demands that LMMs understand the graphical structures accurately and perform multi-step reasoning on the visual graph. Additionally, exploring multimodal graph theory problems will lead to more effective strategies in fields like biology, transportation, and robotics planning. To step forward in this direction, we are the first to design a benchmark named VisionGraph, used to explore the capabilities of advanced LMMs in solving multimodal graph theory problems. It encompasses eight complex graph problem tasks, from connectivity to shortest path problems. Subsequently, we present a Description-Program-Reasoning (DPR) chain to enhance the logical accuracy of reasoning processes through graphical structure description generation and algorithm-aware multi-step reasoning. Our extensive study shows that 1) GPT-4V outperforms Gemini Pro in multi-step graph reasoning; 2) All LMMs exhibit inferior perception accuracy for graphical structures, whether in zero/few-shot settings or with supervised fine-tuning (SFT), which further affects problem-solving performance; 3) DPR significantly improves the multi-step graph reasoning capabilities of LMMs and the GPT-4V (DPR) agent achieves SOTA performance.
Related papers
- Can Graph Learning Improve Task Planning? [61.47027387839096]
Task planning is emerging as an important research topic alongside the development of large language models (LLMs)
In this paper, we explore graph learning-based methods for task planning.
Our approach complements prompt engineering and fine-tuning techniques, with performance further enhanced by improved prompts or a fine-tuned model.
arXiv Detail & Related papers (2024-05-29T14:26:24Z) - Describe-then-Reason: Improving Multimodal Mathematical Reasoning through Visual Comprehension Training [24.989732666940153]
Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs.
MLLMs still struggle with complex multimodal mathematical reasoning, lagging behind proprietary models like GPT-4V(ision) and Gemini-Pro.
We propose a two-step training pipeline VCAR, which emphasizes the Visual Reasoning training in addition to mathematical learning.
arXiv Detail & Related papers (2024-04-22T21:59:35Z) - GraphWiz: An Instruction-Following Language Model for Graph Problems [39.656196336071275]
We introduce GraphInstruct, a dataset designed to equip language models with the ability to tackle a broad spectrum of graph problems using explicit reasoning paths.
We build GraphWiz, an open-source language model capable of resolving various graph problem types while generating clear reasoning processes.
The enhanced model, GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with different complexity levels, surpassing GPT-4 which has an average accuracy of 43.8%.
arXiv Detail & Related papers (2024-02-25T08:41:32Z) - When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding
and Reasoning [54.84870836443311]
The paper presents a new paradigm for understanding and reasoning about graph data by integrating image encoding and multimodal technologies.
This approach enables the comprehension of graph data through an instruction-response format, utilizing GPT-4V's advanced capabilities.
The study evaluates this paradigm on various graph types, highlighting the model's strengths and weaknesses, particularly in Chinese OCR performance and complex reasoning tasks.
arXiv Detail & Related papers (2023-12-16T08:14:11Z) - GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [7.218768686958888]
GraphLLM is a pioneering end-to-end approach that integrates graph learning models with Large Language Models.
Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM.
The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45%.
arXiv Detail & Related papers (2023-10-09T16:42:00Z) - Talk like a Graph: Encoding Graphs for Large Language Models [15.652881653332194]
We study the first comprehensive study of encoding graph-structured data as text for consumption by large language models (LLMs)
We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered.
arXiv Detail & Related papers (2023-10-06T19:55:21Z) - Can Language Models Solve Graph Problems in Natural Language? [51.28850846990929]
Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures.
We propose NLGraph, a benchmark of graph-based problem solving simulating in natural language.
arXiv Detail & Related papers (2023-05-17T08:29:21Z) - Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via
Prompt Augmented by ChatGPT [10.879701971582502]
We aim to develop a large language model (LLM) with the reasoning ability on complex graph data.
Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools.
arXiv Detail & Related papers (2023-04-10T05:25:54Z) - Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming [48.99614465020678]
We introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming.
This mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales.
We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently.
arXiv Detail & Related papers (2021-11-20T22:45:53Z) - Iterative Deep Graph Learning for Graph Neural Networks: Better and
Robust Node Embeddings [53.58077686470096]
We propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL) for jointly and iteratively learning graph structure and graph embedding.
Our experiments show that our proposed IDGL models can consistently outperform or match the state-of-the-art baselines.
arXiv Detail & Related papers (2020-06-21T19:49:15Z)
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.