SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities

• URL: http://arxiv.org/abs/2404.13710v1
• Date: Sun, 21 Apr 2024 16:44:52 GMT
• Title: SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities
• Authors: Kunato Nishina, Yusuke Matsui,
• Abstract summary: Large Language Models can directly process SVG code. SVGEditBench is a benchmark for assessing the LLMs' ability to edit SVG code. GPT-4 showed superior performance to GPT-3.5 both quantitatively and qualitatively.
• Score: 12.555117983678624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image models have shown progress in recent years. Along with this progress, generating vector graphics from text has also advanced. SVG is a popular format for vector graphics, and SVG represents a scene with XML text. Therefore, Large Language Models can directly process SVG code. Taking this into account, we focused on editing SVG with LLMs. For quantitative evaluation of LLMs' ability to edit SVG, we propose SVGEditBench. SVGEditBench is a benchmark for assessing the LLMs' ability to edit SVG code. We also show the GPT-4 and GPT-3.5 results when evaluated on the proposed benchmark. In the experiments, GPT-4 showed superior performance to GPT-3.5 both quantitatively and qualitatively. The dataset is available at https://github.com/mti-lab/SVGEditBench.

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