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ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning

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arxiv 2401.02384 v3 pith:46H2ZY75 submitted 2024-01-04 cs.CV

Fanqing Meng , Wenqi Shao , Quanfeng Lu , Peng Gao , Kaipeng Zhang , Yu Qiao , Ping Luo This is my paper
classification cs.CV
keywords chartdatachartassistantbarschallengeschart-to-tablecomprehensionmodel
verification ladder T0 review T1 audit T2 compute T3 formal
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Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels, legends) poses challenges for general-purpose multimodal models. While vision-language models trained on chart data excel in comprehension, they struggle with generalization. To address these challenges, we propose ChartAssistant, a chart-based vision-language model for universal chart comprehension and reasoning. ChartAssistant leverages ChartSFT, a comprehensive dataset covering diverse chart-related tasks with basic (e.g. bars and pies) and specialized (e.g. radars, and bubbles) chart types. It undergoes a two-stage training process, starting with pre-training on chart-to-table parsing to align chart and text, followed by multitask instruction-following fine-tuning. This approach enables ChartAssistant to achieve competitive performance across various chart tasks. Experimental results demonstrate significant performance gains over the state-of-the-art UniChart and Chartllama method, especially outperforming them on real-world chart data with zero-shot setting. The code and data are available at https://github.com/OpenGVLab/ChartAst.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    cs.CV 2025-08 conditional novelty 7.0 of 10

    A new 565K-pair chart-caption dataset with schema-based dense captions and a reference-free visual consistency metric improves VLM captioning and reduces hallucination.

  2. Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework

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    Introduces a benchmark for MLLM-based chart data extraction from unlabeled images and a human-centered training framework that reaches SOTA numerical accuracy with a 7B model.

  3. ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    ChartFI-Bench supplies 896 chart-description pairs from visually complex charts and defines four metrics (Faithfulness, Coverage, Informativeness, Acuity) aligned to four quality dimensions to evaluate MLLM-generated ...

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    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    ChartFI-Bench supplies 896 chart-description pairs and four metrics (Faithfulness, Coverage, Informativeness, Acuity) to evaluate MLLM-generated chart descriptions on faithfulness and insightfulness.

  5. Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation

    cs.CV 2026-02 conditional novelty 6.0 of 10

    A 7B VLM trained with a structured chart-specification reward beats larger and commercial models on chart-to-code benchmarks using only 3K-4K training samples.

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    cs.CV 2026-01 conditional novelty 6.0 of 10

    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.

  7. CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation

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    CycleChart is a consistency-based framework that organizes chart generation, schema parsing, data parsing, and QA around single data instances to enforce bidirectional semantic alignment and improve cross-task generalization.

  8. Visual Programmability: A Guide for Code-as-Thought in Chart Understanding

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A vision-language model learns to dynamically switch between code-based and visual reasoning for chart questions, improving average accuracy by about one point over fixed strategies.

  9. FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models

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  10. Adaptive Sparse Softmax: An Effective and Efficient Softmax Variant

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