Papers with chart-to-code generation

2 papers
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025.acl-long)

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Challenge: Existing open-source MLLMs fail to fully capture dense information embedded in charts . current models still face significant challenges in understanding and analyzing visual tasks such as captioning and question answering.
Approach: They propose a chart-to-code MLLM which leverages Code LLMs as the language backbone to enhance the executability of the generated code.
Outcome: The proposed model surpasses existing open-source models on chart-to-code benchmarks with only 7B parameters and provides lossless representations that contain all critical details.
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

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Challenge: Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach: They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome: The proposed framework outperforms open-source baselines and is competitive with GPT-5.

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