Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction

Understand

Recently, multimodal large language models (MLLMs) have attracted increasing research attention due to their powerful visual understanding capabilities.

• While they have achieved impressive results on various vision tasks, their performance on chart-to-code generation remains suboptimal.
• This task requires MLLMs to generate executable code that can reproduce a given chart, demanding not only precise visual understanding but also accurate translation of visual elements into structured code.
• Directly prompting MLLMs to perform this complex task often yields unsatisfactory results.