Researchers have introduced a new supervision framework called Observation-Aligned supervision to improve chart-to-code generation models. This method addresses the issue where models are trained on reference plotting scripts that assume fully observable targets, which is often not the case for visual data like charts. The new framework replaces latent raw-data targets with quantities that are directly constrained by the visual information present in the chart, such as summary statistics for boxplots or proportions for pie charts. Experiments using this framework on datasets like ChartMimic and ChartX have shown consistent improvements in models' ability to recover observable values. AI
IMPACT This new supervision technique could lead to more accurate and reliable AI models for generating code from visual data like charts.
RANK_REASON The cluster contains a research paper detailing a new method for AI model training.
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- ChartMimic
- ChartX
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Observation-Aligned supervision
- ScienceCast
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