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相关概念视频

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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相关实验视频

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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思想图表:通过结构化数据提取来提高LLM可视化素养

Amit Kumar Das, Mohammad Tarun, Klaus Mueller

    IEEE transactions on visualization and computer graphics
    |November 20, 2025
    PubMed
    概括
    此摘要是机器生成的。

    大型语言模型 (LLM) 显示了使用新的思想图表提示方法的高级可视化素养. 这种结构化的方法使LLM能够在可视化解释任务上超越人类的表现.

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    科学领域:

    • 人工智能的人工智能
    • 人与计算机的交互
    • 数据可视化 数据可视化

    背景情况:

    • 评估大型语言模型 (LLM) 的可视化素养对于它们在数据解释中的应用至关重要.
    • 现有的提示技术可能无法充分利用LLM在视觉数据分析方面的潜力.

    研究的目的:

    • 评估最先进的法学士的可视化素养.
    • 引入并评估思想图表提示技术的有效性.

    主要方法:

    • 三个LLM (Claude-3.7-sonnet,GPT-4.5-preview,Gemini-2.0-pro) 在可视化识字评估测试 (VLAT) 中进行了测试.
    • 开发了一种新的提示技术,即思想图表,用于通过系统的数据提取,验证和分析来指导LLM.
    • 标准提示和思想图表方法之间的性能进行了比较.

    主要成果:

    • 克劳德-3.7-sonnet使用思想图表获得了50.17的VLAT得分,明显超过了人类基线的28.82.
    • 思想图表方法改善了LLM的表现,GPT-4.5的得分增加了21.8%,Gemini-2.0的得分增加了9.4%,Claude-3.7的得分增加了13.5%.
    • 克劳德-3.7-索内特在几个具有挑战性的图表类型上实现了100%的准确性,在可视化解释方面取得了实质性的收益.

    结论:

    • 现代多式联络LLM可以通过适当的分析框架在可视化素养方面超过人类的表现.
    • 结构化的提示策略,如思想图表,对于LLMs复杂的视觉解释至关重要.
    • 这种方法对提高LLM能力和提高可视化可访问性有影响.