NL2Color:对自然语言图表的颜色调色板进行完善
IEEE transactions on visualization and computer graphics
|October 23, 2023
概括
新手用户现在可以使用自然语言轻松创建有效的图表色调板. 由大型语言模型 (LLM) 驱动的NL2Color工具以最小的用户精力完善图表美学.
科学领域:
- 数据可视化 数据可视化
- 人与计算机的交互
- 计算设计的计算设计.
背景情况:
- 有效的图表色调板对于数据可视化至关重要,但对于初学者设计师来说具有挑战性.
- 用户经常难以将设计意图转化为图表颜色的可操作的编辑步骤.
研究的目的:
- 介绍NL2Color,这是一种新的工具,使初学者能够使用自然语言来改进图表的色调板.
- 为了简化实现所需图表美学的过程,对于没有设计专业知识的用户来说.
主要方法:
- 收集并对131个图表颜色调色板示例的数据集进行了分类,并提供了相应的编辑意图和专家设计的修订.
- 使用一个大语言模型 (LLM) 带有几次拍摄提示,以基于自然语言输入生成新的色调板.
- 进行了两阶段的评估,包括众包研究 (N=71) 和用户研究 (N=12).
主要成果:
- NL2与人类专家创建的颜色调色板相比,颜色调色板在质量上没有显著差异.
- 用户对修订后的调色板表示满意,用更少的时间和精力实现了所需的结果.
- 该工具有效地将自然语言描述转化为功能图的颜色调整.
结论:
- NL2Color使初学者能够通过直观的自然语言交互来创建高质量的图表色调板.
- 该工具展示了LLM在数据可视化中弥合用户意图和设计执行之间的差距方面的潜力.
- NL2Color为改善图表设计的可访问性和效率提供了一个有希望的解决方案.
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