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About this paper
Saadiq Rauf Khan, Vinit Chandak, Sougata Mukherjea
This research provides a systematic evaluation of four leading Large Language Models for their capabilities in generating visualization code and comprehending visual data. The study from IIT Delhi found GPT-4o demonstrated the highest accuracy in generating Python visualization code and showed superior understanding of pie charts, though all models faced challenges with complex visual reasoning and less common chart types.
Key insights
- LLM performance varies significantly across visualization tasks, with GPT-4o generally outperforming other models in both code generation and visual comprehension.
- LLMs exhibit stronger capabilities in generating Python-based visualization code compared to declarative languages like Vega-Lite, and struggle with less common chart types.
- Visual comprehension by LLMs is challenged by fine-grained distinctions, spatial relationships, and specific chart types like line charts, although system prompts can consistently improve performance.
Summary by alphaXiv.
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