Search papers, labs, and topics across Lattice.
The paper introduces Chart2NCode, a dataset of 176K charts paired with aligned plotting scripts in Python, R, and LaTeX, enabling multi-language chart-to-code generation. To effectively leverage this data, they propose CharLuMA, a LLaVA-style architecture with a language-conditioned mixture of low-rank subspaces for parameter-efficient adaptation to different plotting languages. Experiments demonstrate that CharLuMA achieves improved executability and visual fidelity across all languages compared to existing baselines, highlighting the benefits of multi-language supervision.
Training on semantically equivalent chart renderings in Python, R, and LaTeX unlocks surprisingly effective multi-lingual chart-to-code generation from a single model.
Chart-to-code generation converts a chart image into an executable plotting script, enabling faithful reproduction and editable visualizations. Existing methods are largely Python-centric, limiting practical use and overlooking a critical source of supervision: the same chart can be expressed by semantically equivalent scripts in different plotting languages. To fill this gap, we introduce Chart2NCode, a dataset of 176K charts paired with aligned scripts in Python, R, and LaTeX that render visually equivalent outputs, constructed via a metadata-to-template pipeline with rendering verification and human quality checks. Building on a LLaVA-style architecture, we further propose CharLuMA, a parameter-efficient adaptation module that augments the multimodal projector with a language-conditioned mixture of low-rank subspaces, allowing the model to share core chart understanding while specializing code generation to the target language through lightweight routing. Extensive experiments show consistent gains in executability and visual fidelity across all languages, outperforming strong open-source baselines and remaining competitive with proprietary systems. Further analyses reveal that balanced multi-language supervision benefits all languages and that the adapter allocates a compact shared core plus language-specific capacity. Codes and data are available at https://github.com/Zhihan72/CharLuMA.
