Interactive Text-to-Visualization: Refining Visualization Outputs Through Natural Language User Feedback
Xubang Xiong, Raymond Chi-Wing Wong, Yuanfeng Song · 2025
Data visualization (DV) is of significance to the data analysis applications, exploring the hidden patterns and showing the insightful data. The task of Text-to-Vis, which takes text as input and generates data visualizations, was proposed to lower the threshold for generating DVs. However, the existing methods only view this problem as a one-shot mapping problem, directly outputting the final DVs without considering any user feedback for refining the generated DVs. Motivated by this, a more interactive scenario is investigated, where users could provide natural language feedback to refine the generated DVs. The scenario is formulated as the Text-to-Vis with Feedback problem. A new dataset is also created to further study the problem, which contains the user utterance, database schema, generated DVs, the natural language feedback and the refined DVs. A large language model (LLM) based framework named Vis-Edit is designed for handling this task, including schema linking, clause location, clause generation, merger and self-consistency. Eventually, extensive experiments reveal the effectiveness of Vis-Edit.