ChartCF achieves strong chart understanding performance in VLMs using significantly less training data by generating code-based counterfactuals, selecting similar samples, and performing multimodal preference optimization.
ECD misidentifies the target variable by confusing similar visual markers, while ChartCF correctly identifies “b_3” through precise spatial reasoning
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Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding
ChartCF achieves strong chart understanding performance in VLMs using significantly less training data by generating code-based counterfactuals, selecting similar samples, and performing multimodal preference optimization.