# Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way

arXiv: https://arxiv.org/abs/2408.16119 · HTML: https://mlsanity.com/paper/2408.16119/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 73 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 29 of 29)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [RULER: What's the Real Context Size of Your Long-Context Language Models?](https://mlsanity.com/paper/2404.06654/) (2024)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement](https://mlsanity.com/paper/2402.14658/) (2024)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Is Self-Repair a Silver Bullet for Code Generation?](https://mlsanity.com/paper/2306.09896/) (2023)
- [Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs](https://mlsanity.com/paper/2311.02262/) (2023)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models](https://mlsanity.com/paper/2310.03691/) (2023)
- [On the Design of AI-powered Code Assistants for Notebooks](https://mlsanity.com/paper/2301.11178/) (2023)
- [How Do Analysts Understand and Verify AI-Assisted Data Analyses?](https://mlsanity.com/paper/2309.10947/) (2023)
- [Multi-modal Synthesis of Regular Expressions](https://mlsanity.com/paper/1908.03316/) (2019)
- [mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks](https://mlsanity.com/paper/2009.10643/) (2020)
- [Facilitating Conversational Interaction in Natural Language Interfaces for Visualization](https://mlsanity.com/paper/2207.00189/) (2022)
- [Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows](https://mlsanity.com/paper/2105.00121/) (2021)
- [Falx: Synthesis-Powered Visualization Authoring](https://mlsanity.com/paper/2102.01024/) (2021)
- [Type-Directed Synthesis of Visualizations from Natural Language Queries](https://mlsanity.com/paper/2209.01081/) (2022)
- [Task-Oriented Optimal Sequencing of Visualization Charts](https://mlsanity.com/paper/1908.02502/) (2019)
- [Visualizing the Scripts of Data Wrangling with SOMNUS](https://mlsanity.com/paper/2209.13981/) (2022)

## Built on by (most cited first, 21 of 21)

- [A Survey on Large Language Model-based Agents for Statistics and Data Science](https://mlsanity.com/paper/2412.14222/) (2024)
- [AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science](https://mlsanity.com/paper/2502.16395/) (2025)
- [LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis](https://mlsanity.com/paper/2605.30434/) (2026)
- [Challenges & Opportunities with LLM-Assisted Visualization Retargeting](https://mlsanity.com/paper/2507.01436/) (2025)
- [ORCA: ORchestrating Causal Agent](https://mlsanity.com/paper/2508.21304/) (2025)
- [A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations](https://mlsanity.com/paper/2509.16454/) (2025)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)
- [NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs](https://mlsanity.com/paper/2601.06126/) (2026)
- [VegaChat: A Robust Framework for LLM-Based Chart Generation and Assessment](https://mlsanity.com/paper/2601.15385/) (2026)
- [DA-Studio: An Agentic System for End-to-End Data Analysis](https://mlsanity.com/paper/2606.31423/) (2026)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [Toward a Machine Bertin: Why Visualization Needs Design Principles for Machine Cognition](https://mlsanity.com/paper/2602.01527/) (2026)
- [CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization](https://mlsanity.com/paper/2602.09851/) (2026)
- [Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specifications](https://mlsanity.com/paper/2604.07652/) (2026)
- [Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy](https://mlsanity.com/paper/2606.03142/) (2026)
- [MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems](https://mlsanity.com/paper/2608.16181/) (2026)
- [AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis](https://mlsanity.com/paper/2608.17834/) (2026)
- [Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation](https://mlsanity.com/paper/2610.00374/) (2026)
- [Where LLMs Fail with Visualization DSLs](https://mlsanity.com/paper/2610.01873/) (2026)
- [DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis](https://mlsanity.com/paper/2610.02679/) (2026)
- [A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain](https://mlsanity.com/paper/2610.06914/) (2026)

## Abstract (arXiv)

Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis. Although many AI-powered systems have been introduced to reduce the effort of visualization authoring, existing systems are not well suited for iterative authoring. They typically require analysts to provide, in a single turn, a text-only prompt that fully describe a complex visualization. We introduce Data Formulator 2 (DF2 for short), an AI-powered visualization system designed to overcome this limitation. DF2 blends graphical user interfaces and natural language inputs to enable users to convey their intent more effectively, while delegating data transformation to AI. Furthermore, to support efficient iteration, DF2 lets users navigate their iteration history and reuse previous designs, eliminating the need to start from scratch each time. A user study with eight participants demonstrated that DF2 allowed participants to develop their own iteration styles to complete challenging data exploration sessions.