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Related Concept Videos

Language Development01:22

Language Development

810
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
810

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Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction.

Amit Kumar Das, Mohammad Tarun, Klaus Mueller

    IEEE Transactions on Visualization and Computer Graphics
    |November 20, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Large Language Models (LLMs) show advanced visualization literacy using the novel Charts-of-Thought prompting method. This structured approach enables LLMs to surpass human performance on visualization interpretation tasks.

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    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Assessing Large Language Models' (LLMs) visualization literacy is crucial for their application in data interpretation.
    • Existing prompting techniques may not fully leverage LLMs' potential for visual data analysis.

    Purpose of the Study:

    • To evaluate the visualization literacy of state-of-the-art LLMs.
    • To introduce and assess the effectiveness of the Charts-of-Thought prompting technique.

    Main Methods:

    • Three LLMs (Claude-3.7-sonnet, GPT-4.5-preview, Gemini-2.0-pro) were tested on the Visualization Literacy Assessment Test (VLAT).
    • A novel prompting technique, Charts-of-Thought, was developed to guide LLMs through systematic data extraction, verification, and analysis.
    • Performance was compared between standard prompts and the Charts-of-Thought method.

    Main Results:

    • Claude-3.7-sonnet achieved a VLAT score of 50.17 using Charts-of-Thought, significantly exceeding the human baseline of 28.82.
    • The Charts-of-Thought method improved LLM performance, with score increases of 21.8% for GPT-4.5, 9.4% for Gemini-2.0, and 13.5% for Claude-3.7.
    • Claude-3.7-sonnet achieved 100% accuracy on several challenging chart types, demonstrating substantial gains in visualization interpretation.

    Conclusions:

    • Modern multimodal LLMs can exceed human performance in visualization literacy with appropriate analytical frameworks.
    • Structured prompting strategies like Charts-of-Thought are vital for complex visual interpretation by LLMs.
    • This method has implications for improving LLM capabilities and enhancing visualization accessibility.