Geodesic
Vector graphics generation with LLMs: approaches and models
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part II–2, Tome 530 (2023), pp. 24-37

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The task of generating vector graphics with AI is under-researched. Recently, large language models (LLMs) have been successfully applied to many downstream tasks. For example, modern LLMs achieve remarkable quality in code generation tasks and are open for public access. This study compares approaches to vector graphics generation with LLMs, namely ChatGPT (GPT-3.5) and GPT-4. GPT-4 has noticeable improvements compared to ChatGPT. Both models easily generate geometric primitives but struggle even with simple objects. The results produced by GPT-4 visually resemble the prompts but are inaccurate. GPT-4 is able to correct the output according to instructions. Additionally, it is challenging for both models to recognize an object from an SVG image. Both models recognize only primitive objects correctly. 
@article{ZNSL_2023_530_a2,
     author = {B. Timofeenko and V. Efimova and A. Filchenkov},
     title = {Vector graphics generation with {LLMs:} approaches and models},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {24--37},
     publisher = {mathdoc},
     volume = {530},
     year = {2023},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2023_530_a2/}
}
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B. Timofeenko; V. Efimova; A. Filchenkov. Vector graphics generation with LLMs: approaches and models. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part II–2, Tome 530 (2023), pp. 24-37. http://geodesic.mathdoc.fr/item/ZNSL_2023_530_a2/