VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
This work addresses the need for standardized evaluation in vector graphics processing for researchers and developers, though it is incremental as it builds on existing qualitative studies by introducing a quantitative benchmark.
The paper tackles the problem of evaluating large language models (LLMs) on vector graphics understanding and generation, proposing VGBench as a comprehensive benchmark with 4279 understanding and 5845 generation samples, and finds that LLMs show strong capability but less desirable performance on low-level formats like SVG.
In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual content, especially for designers and artists who depict the world using geometry primitives such as polygons. Vector graphics (VG), on the other hand, offer a textual representation of visual content, which can be more concise and powerful for content like cartoons, sketches and scientific figures. Recent studies have shown promising results on processing vector graphics with capable Large Language Models (LLMs). However, such works focus solely on qualitative results, understanding, or a specific type of vector graphics. We propose VGBench, a comprehensive benchmark for LLMs on handling vector graphics through diverse aspects, including (a) both visual understanding and generation, (b) evaluation of various vector graphics formats, (c) diverse question types, (d) wide range of prompting techniques, (e) under multiple LLMs and (f) comparison with VLMs on rasterized representations. Evaluating on our collected 4279 understanding and 5845 generation samples, we find that LLMs show strong capability on both aspects while exhibiting less desirable performance on low-level formats (SVG). Both data and evaluation pipeline will be open-sourced at https://vgbench.github.io.