Data Formulator: AI-Powered Concept-Driven Visualization Authoring
Chenglong Wang, John Thompson, Bongshin Lee
摘要
With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper11
- nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent WorkflowGeliang Ouyang, Jingyao Chen, Zhihe Nie, Yi Gui 等ACL 2025 · 被引用 22 次
- Dango: A Mixed-Initiative Data Wrangling System using Large Language ModelWei-Hao Chen, Weixi Tong, Amanda Case, Tianyi ZhangCHI 2025 · 被引用 19 次
- Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model PromptsSam Yu-Te Lee, Aryaman Bahukhandi, Dongyu Liu, Kwan-Liu MaIEEE VIS 2024 · 被引用 18 次
- Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the WayChenglong Wang, Bongshin Lee, Steven Mark Drucker, Dan Marshall 等CHI 2025 · 被引用 17 次
- Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and TasksAstrid van den Brandt, Sehi L'Yi, Huyen N. Nguyen, Anna Vilanova 等IEEE VIS 2024 · 被引用 12 次
它引用的顶会 Paper24
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- DS-1000: A Natural and Reliable Benchmark for Data Science Code GenerationYuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang 等ICML 2023 · 被引用 504 次
- Grounded Copilot: How Programmers Interact with Code-Generating ModelsShraddha Barke, Michael B. James, Nadia PolikarpovaOOPSLA 2023 · 被引用 408 次
- NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language QueriesArpit Narechania, Arjun Srinivasan, John T. StaskoIEEE VIS 2020 · 被引用 210 次
- Synchromesh: Reliable Code Generation from Pre-trained Language ModelsGabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari 等ICLR 2022 · 被引用 200 次
相关 Paper
- DataWink: Reusing and Adapting SVG-Based Visualization Examples with Large Multimodal ModelsLiwenhan Xie, Yanna Lin, Can Liu, Huamin Qu 等IEEE VIS 2025 · 被引用 3 次
- Falx: Synthesis-Powered Visualization AuthoringChenglong Wang, Yu Feng, Rastislav Bodík, Isil Dillig 等CHI 2021 · 被引用 35 次
- Towards Natural Language-Based Visualization AuthoringYun Wang, Zhitao Hou, Leixian Shen, Tongshuang Wu 等IEEE VIS 2022 · 被引用 74 次
- Reflecting on Design Paradigms of Animated Data Video ToolsLeixian Shen, Haotian Li, Yun Wang, Huamin QuCHI 2025 · 被引用 10 次
- InfoAlign: A Human-AI Co-Creation System for Storytelling with InfographicsJielin Feng, Xinwu Ye, Qianhui Li, Verena Ingrid Prantl 等CHI 2026 · 被引用 1 次
