氛围分析：探索数据可视化从业者对大型语言模型的采用

中文摘要 AI 辅助

大型语言模型（LLMs）因其有望通过更快、更简单的数据准备、分析和可视化创建工作流程来支持数据可视化（Vis）而颇具吸引力。然而，LLMs 以易出错著称，且并非为数据可视化任务而设计。很少有研究探讨 Vis 从业者对 LLMs 的采用情况。为填补这一空白，我们对数据可视化协会（一个全球性的数据可视化设计师社区）的成员进行了半结构化访谈。我们的研究结果表明，Vis 设计师在可视化过程的创意和技术方面都积极使用 LLMs。一种新的可视化工作流程正在涌现，我们称之为“氛围分析”，类似于“氛围编码”。参与者提出的一些关键挑战与氛围编码的挑战相似，而其他挑战则是 Vis 特有的，例如 Vis 知识空白和图表验证。这项工作为结合 LLM 中介工作与融入数据可视化指导、约束和最佳实践的 Vis 工具的研究开辟了机遇。

英文摘要

Large language models (LLMs) are enticing in their promise to support data visualization (Vis) through faster and simpler workflows for data prep, analysis, and visualization creation. Yet LLMs are notoriously error-prone and not built for data visualization tasks. Few studies have explored LLM adoption among Vis practitioners. To fill this gap, we conducted semi-structured interviews with members of the Data Visualization Society, a global community of data visualization designers. Our findings show that Vis designers actively use LLMs for both creative and technical aspects of the visualization process. A new visualization workflow is emerging, a process we call vibe analysis, analogous to vibe coding. Some key challenges raised by participants parallel those of vibe coding, while others are Vis-specific, like gaps in Vis knowledge and chart verification. This work opens up opportunities for research combining LLM-mediated work with Vis tools that incorporate data visualization guidance, constraints, and best practices.

发表机构

• Northeastern University（东北大学）

机构由 AI 辅助整理，请以论文原文为准。