$C^2$: Scalable Auto-Feedback for LLM-based Chart Generation

Woosung Koh; Jang Han Yoon; MinHyung Lee; Youngjin Song; Jaegwan Cho; Jaehyun Kang; Taehyeon Kim; Se-Young Yun; Youngjae Yu; Bongshin Lee

arXiv · 2410.18652

$C^2$: Scalable Auto-Feedback for LLM-based Chart Generation

Woosung Koh·Jang Han Yoon·MinHyung Lee·Youngjin Song·Jaegwan Cho·Jaehyun Kang·Taehyeon Kim·Se-Young Yun·Youngjae Yu·Bongshin Lee

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Abstract

Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \text{instruction}, \text{data}, \text{code} \rangle$ triplets are scarce and expensive to manually curate as their creation demands technical expertise. To address this scalability challenge, we introduce a reference-free automatic feedback generator, which eliminates the need for costly human intervention. Our novel framework, C$^2$, consists of (1) an automatic feedback provider (ChartAF) and (2) a diverse, reference-free dataset (ChartUIE-8K). The results are compelling: in our first experiment, 74% of respondents strongly preferred, and 10% preferred, the results after feedback. The second post-feedback experiment demonstrates that ChartAF outperform nine baselines. Moreover, ChartUIE-8K significantly improves data diversity by increasing queries, datasets, and chart types by 5982%, 1936%, and 91%, respectively, over benchmarks. Finally, a study of LLM users revealed that 94% of participants preferred ChartUIE-8K's queries, with 93% deeming them aligned with real-world use cases. Core contributions are available as open-source at chartsquared.github.io, with ample qualitative examples.

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Woosung Koh, Jang Han Yoon, MinHyung Lee, Youngjin Song, Jaegwan Cho, Jaehyun Kang, Taehyeon Kim, Se-Young Yun, Youngjae Yu, Bongshin Lee. 2024-10-24. $C^2$: Scalable Auto-Feedback for LLM-based Chart Generation. https://arxiv.org/abs/2410.18652

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