主动VA:主动视觉分析与LLM-基于UI代理
IEEE transactions on visualization and computer graphics
|December 15, 2025
概括
本研究介绍了ProactiveVA,这是一种智能框架,使用大型语言模型 (LLM) 驱动的代理来主动帮助用户使用视觉分析 (VA) 工具. 它提供了上下文意识的帮助,改善了复杂数据分析中的用户体验.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 视觉分析 (VA) 工具对于复杂的数据分析至关重要,但对用户来说可能是压倒性的.
- 目前在VA的智能辅助是反应性的,只有在明确要求时才提供帮助.
- 在VA系统中,需要更加主动和上下文意识的智能辅助.
研究的目的:
- 提出和评估ProactiveVA,这是一个用于视觉分析中的主动智能辅助的新框架.
- 设计一个由LLM驱动的UI代理,监控用户交互并提供上下文感知的帮助.
- 通过在用户最需要时提供支持来解决现有的VA援助的局限性.
主要方法:
- 进行了形成性研究,分析用户交互日志,以了解寻求帮助的行为.
- 蒸的关键设计要求:意图识别,解决方案生成,可解释性和可控制性.
- 开发了一个三阶段的UI代理管道 (感知,推理,行动) 用于自主用户需求评估和协助交付.
主要成果:
- 在两个代表的VA系统中实施并证明了ProactiveVA框架的通用性.
- 通过算法评估,案例/专家研究和用户研究来评估有效性.
- 确定了关键的设计权衡和未来在主动VA方面的研究领域.
结论:
- 积极的VA有效地提供上下文意识的协助,增强视觉分析用户体验.
- 拟议的LLM驱动的UI代理可以自主感知用户需求并提供量身定制的指导.
- 需要进一步的研究来探索设计的权衡,并推进VA的积极援助.
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