Summary of Vgbench: Evaluating Large Language Models on Vector Graphics Understanding and Generation, by Bocheng Zou et al.
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
by Bocheng Zou, Mu Cai, Jianrui Zhang, Yong Jae Lee
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: The paper proposes VGBench, a comprehensive benchmark for Large Language Models (LLMs) to evaluate their capability in processing vector graphics. Unlike traditional rasterized representations, vector graphics offer a textual representation of visual content, which can be more concise and powerful for specific types of content like cartoons, sketches, and scientific figures. Recent studies have shown promising results on processing vector graphics with LLMs, but these works focus solely on qualitative results or specific types of vector graphics. VGBench aims to fill this gap by providing a diverse set of benchmarks, including visual understanding, generation, various formats, question types, prompting techniques, and multiple LLMs. The authors collected 4279 understanding samples and 5845 generation samples, finding that LLMs show strong capability on both aspects while exhibiting less desirable performance on low-level formats (SVG). The data and evaluation pipeline will be open-sourced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making computers better at understanding and creating vector graphics. Vector graphics are like cartoons or sketches, but instead of being made up of tiny squares called pixels, they’re made up of shapes like lines and curves. Right now, computers can only understand pixel-based images well. But what if we could teach them to understand and create vector graphics too? That’s what the VGBench project is all about – testing how well computers can do this job. The researchers found that some computer programs are really good at understanding and creating vector graphics, but they’re not perfect yet. They hope that by sharing their test data and methods, other people will be able to improve on their work. |
Keywords
» Artificial intelligence » Prompting
