发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈12位专家，发现视觉数据故事叙述者选择性委托执行任务给LLM，保留叙事意图控制，LLM辅助在人类设定后最有效，并提出了边界感知工具的设计启示。
AI 中文摘要
尽管大型语言模型（LLM）已出现于视觉数据故事叙述工作流程中，但关于作者如何决定将哪些活动或任务委托给它们，以及哪些内容应“受保护”或保持在人类控制之下，仍存在开放性问题。为调查此问题，我们访谈了12位专家级视觉数据故事叙述者。分析显示，参与者很少将LLM视为自主的故事叙述者。相反，他们倾向于有选择地将执行导向的任务委托给LLM，同时保留对塑造叙事意图和故事意义活动的控制。我们的发现表明，在人类进行初始设定和约束设置之后，LLM辅助最为高效，并且它将劳动从生产转向验证。我们讨论了边界感知创作工具、数据基础生成、低保真构思以及基于LLM的可视化研究报告实践的设计启示。本文的补充材料可在此https URL获取。
英文摘要
Despite the emergence of large language models (LLMs) for visual data storytelling workflows, there are open questions about how authors decide what activities or tasks to entrust to them and what should be "protected" or maintained under human control. To investigate this, we interviewed a cohort of 12 expert visual data storytellers. Our analysis shows that participants rarely treated LLMs as autonomous storytellers. Instead, they tend to selectively delegate execution-oriented tasks to LLMs while retaining control over activities that shape narrative intent and story meaning. Our findings show that LLM assistance is most productive after human seeding and constraint-setting, and that it shifts labor from production to verification. We discuss design implications for boundary-aware authoring tools, data-grounded generation, low-fidelity ideation, and reporting practices for LLM-based visualization research. Supplemental materials for this paper are available at https://osf.io/hcnp6.
CommentsAccepted as a Short Paper at IEEE VIS 2026
