为协作设计:可视化使人类-LLM分析伙伴关系成为可能
IEEE computer graphics and applications
|September 29, 2025
概括
动态可视化对于数据分析中的有效人大语言模型 (LLM) 协作至关重要. 可视化不断发展的分析文物和来源增强了LLM辅助工作流程的透明度和洞察力.
科学领域:
- 数据可视化 数据可视化
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 视觉化文物传统上支持人类协作和知识转移在数据分析.
- 视觉化在人类与大型语言模型 (LLM) 交互期间捕获知识的作用仍然未被充分探索.
- 当前的LLM分析工作流通常是线性和基于文本的,阻碍了分析过程的结构化表示.
研究的目的:
- 调查可视化工件在人-LLM分析工作流程中的潜力.
- 要突出目前的LLM基于文本的追踪和结构分析方法的局限性.
- 倡导动态的视觉表示,以增强人类-LLM合作和知识外部化.
主要方法:
- 探索LLM在跟踪,结构化和可视化分析过程中的当前机会和局限性.
- 在人-LLM工作流程中整合动态可视化的概念论证.
- 根据LLM的进展,提出一个研究议程的建议.
主要成果:
- 在LLMs的线性基于文本的工作流程限制了分析文物的可追溯性和结构.
- 动态视觉表示被认为是构建不断变化的文物和来源的关键.
- 展示了使用LLM来可视化分析过程的机遇和局限性.
结论:
- 动态可视化对于有效的人类-LLM分析互动至关重要.
- 可视化不断演变的文物和来源可以导致更结构化和透明的分析过程.
- 需要进一步的研究来利用LLM的功能来增强分析中的可视化.
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