RAGE-Vis：面向基于自然语言的图表编辑的关系感知生成式编辑界面
RAGE-Vis:A Relation-Aware Generative Editing Interface for Natural Language-Based Chart Editing
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中文总结 AI 辅助
针对现有图表编辑方法无法处理复合跨组件请求且易产生全局不一致结果的问题，提出RAGE-Vis界面，通过解析复合意图、识别受影响字段实现跨组件协同控制，经案例与用户研究验证其有效性。
中文摘要 AI 辅助
自然语言为用户表达图表编辑意图提供了简便方式，这类意图通常是复合且跨组件的（例如调整样式、扩展类别、突出数值）。然而现有方法通常将指令映射为单一操作或控件，限制了其处理高层次请求的能力，且因缺乏对图表组件间关系的感知，常产生局部合理但全局不一致的结果。为应对这些挑战，我们提出RAGE-Vis，即面向基于自然语言的图表编辑的关系感知生成式编辑界面。该系统支持位图图表图像作为输入，将其转换为可编辑的参数化中间表示。RAGE-Vis并非将指令映射为单一编辑操作或控件，而是解析复合意图，识别目标与范围，并为未明确的请求生成分层编辑面板，使用户可同时调整全局设置与局部参数。此外，RAGE-Vis基于视觉编码关系、结构关系及表达一致性关系识别潜在受影响字段，并将其组织为可操作控件以支持跨组件协同控制。通过两个案例研究，我们证明了RAGE-Vis在复杂编辑任务中的适用性，包括样式调整、数据扩展、顺序重排、图例布局及颜色映射。用户研究进一步表明，参与者可借助RAGE-Vis有效处理未明确的请求、探索候选替代方案并维持跨组件一致性。
英文摘要
Natural language offers an easy way for users to express chart editing intents, which are often composite and cross-component (e.g., adjusting style, extending categories, highlighting values). However, existing methods typically map instructions to a single operation or widget, limiting their ability to handle high-level requests and often producing locally plausible but globally inconsistent results due to a lack of awareness of relationships between chart components. To address these challenges, we introduce RAGE-Vis, a Relation-Aware Generative Editing interface for natural language-based chart editing. The system supports bitmap chart images as input and converts them into an editable parameterized intermediate representation. Instead of mapping instructions to a single edit or widget, RAGE-Vis parses composite intents, identifies targets and scopes, and generates hierarchical editing panels for underspecified requests, enabling users to adjust both global settings and local parameters. Furthermore, RAGE-Vis identifies potentially affected fields based on visual encoding relations, structural relationships, and expressive consistency relations, and organizes them into actionable widgets to support cross-component coordinated controls. Through two case studies, we demonstrate the applicability of RAGE-Vis in complex editing tasks, including style adjustment, data extension, order rearrangement, legend layout, and color mapping. A user study further shows that participants can effectively handle underspecified requests, explore candidate alternatives, and maintain cross-component consistency with RAGE-Vis.
