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.
In Proceedings of the 61st Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pages 12756–12770
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ChartVerse uses Rollout Posterior Entropy and truth-anchored inverse QA synthesis to produce 640K high-quality chart reasoning samples, training an 8B model that surpasses its 30B teacher.
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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.
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ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch
ChartVerse uses Rollout Posterior Entropy and truth-anchored inverse QA synthesis to produce 640K high-quality chart reasoning samples, training an 8B model that surpasses its 30B teacher.