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LEVA:使用大型语言模型来增强视觉分析

Yuheng Zhao, Yixing Zhang, Yu Zhang

IEEE transactions on visualization and computer graphics

|March 4, 2024

概括

大型语言模型 (LLM) 通过协助用户进行登陆,探索和总结来增强视觉分析 (VA). 一个新的框架LEVA使用LLM来简化复杂的数据分析工作流程.

科学领域:

计算机科学计算机科学
人与计算机的交互

背景情况:

视觉分析 (VA) 对于复杂的数据分析至关重要,但由于数据类型和交互的多样性,需要用户的大量认知负载.
现有的VA方法需要改进智能支持,以应对信息处理挑战.

研究的目的:

引入LEVA,一个利用大型语言模型 (LLM) 的框架,以增强视觉分析中的用户工作流.
提高VA流程的多个阶段的用户效率和有效性:登陆,探索和总结.

主要方法:

莱瓦利用LLM来解释可视化设计和用户登陆关系.
根据系统状态和数据分析,LLM建议提供见解,以促进混合倡议的探索.
选择性报告策略与LLM相结合,通过追溯分析历史,生成洞察性报告.

主要成果:

可以将LEVA集成到现有的视觉分析系统中.
通过两个使用场景和一个用户研究证明了有效性.
LEVA 显著帮助用户进行视觉分析任务.

结论:

大型语言模型为开发更智能的视觉分析系统提供了强大的方法.
在整个视觉分析生命周期中,LEVA框架有效地支持用户.
未来的工作可以探索进一步整合LLMs,以推进在数据分析中的人类-AI合作.

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