DeepVIS:通过循序渐进的推理,将自然语言和数据可视化联系起来
IEEE transactions on visualization and computer graphics
|December 8, 2025
概括
本研究介绍了思维链 (CoT) 推理,以改善自然语言到可视化 (NL2VIS) 系统. CoT框架提高了可视化质量,并为更好的用户控制和改进提供了透明的推理.
科学领域:
- 计算机科学 计算机科学
- 数据可视化 数据可视化
- 人工智能的人工智能
背景情况:
- 数据可视化对于洞察发现至关重要,但需要专业知识,并可能破坏分析工作流程.
- 目前的自然语言可视化 (NL2VIS) 系统缺乏透明度,阻碍了用户对生成的可视化理解和改进.
研究的目的:
- 将思维链 (CoT) 推理整合到NL2VIS管道中,以提高透明度和控制.
- 在NL2VIS中开发创建和利用CoT推理步骤的方法,以改进可视化生成.
主要方法:
- 为NL2VIS设计了一个全面的Cot推理过程,以及用于数据集注释的自动管道.
- 引入了nvBench-CoT,这是一个数据集,包含详细的推理步骤,用于微调NL2VIS模型.
- 开发了DeepVIS,这是一个交互式界面,用于检查和调整可视化生成中的CoT推理.
主要成果:
- CoT框架显著提高了NL2VIS质量,通过nVBench-CoT数据集实现了最先进的性能.
- 量化基准,使用案例和用户研究验证了CoT方法的有效性.
- 通过与推理步骤互动,DeepVIS使用户能够理解,调试和完善可视化输出.
结论:
- 将CoT推理集成到NL2VIS管道中,为自动化可视化提供了一个透明和可控的方法.
- 拟议的框架提高了从自然语言描述中生成的可视化图像的质量和可解释性.
- 这项工作为更加以用户为中心和有效的自动化数据可视化工具铺平了道路.
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