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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
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    This study introduces ChartX, an evaluation set for multi-modal large language models (MLLMs) on chart understanding. ChartVLM, a novel model, demonstrates superior performance on chart-based reasoning tasks compared to existing models.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Multi-modal Large Language Models (MLLMs) are rapidly advancing.
    • The ability of MLLMs to interpret and reason about visual charts is not well-understood.
    • Existing benchmarks do not comprehensively assess MLLMs in the chart domain.

    Purpose of the Study:

    • To rigorously benchmark the chart-related capabilities of current MLLMs.
    • To introduce ChartX, a novel multi-modal evaluation dataset for charts.
    • To develop and evaluate ChartVLM, a model designed for interpretable multi-modal reasoning.

    Main Methods:

    • Construction of ChartX, a diverse evaluation set with 18 chart types, 7 tasks, and 22 topics.
    • Development of ChartVLM, a new model architecture for multi-modal tasks requiring pattern interpretation.
    • Evaluation of mainstream MLLMs and ChartVLM on the ChartX dataset.

    Main Results:

    • ChartVLM significantly outperforms existing versatile and chart-specific MLLMs, including GPT-4V.
    • The proposed ChartX dataset provides a comprehensive benchmark for chart understanding.
    • ChartVLM shows promise for tasks demanding interpretable multi-modal reasoning.

    Conclusions:

    • Current MLLMs have limitations in chart-based information querying and reasoning.
    • ChartVLM offers a new approach for multi-modal tasks, particularly those involving visual patterns.
    • This work facilitates future research in chart evaluation and interpretable multi-modal AI.