# NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries

arXiv: https://arxiv.org/abs/2209.08834 · HTML: https://mlsanity.com/paper/2209.08834/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?](https://mlsanity.com/paper/2010.12725/) (2020)
- [Evaluating the Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2204.00498/) (2022)
- [RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://mlsanity.com/paper/2205.06983/) (2022)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Few-Shot Semantic Parsing with Language Models Trained On Code](https://mlsanity.com/paper/2112.08696/) (2021)
- [Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL](https://mlsanity.com/paper/2010.12634/) (2020)
- [PI2: End-to-end Interactive Visualization Interface Generation from Queries](https://mlsanity.com/paper/2107.08203/) (2021)
- [Monte Carlo Tree Search for Generating Interactive Data Analysis Interfaces](https://mlsanity.com/paper/2001.01902/) (2020)
- [Demonstration of PI2: Interactive Visualization Interface Generation for SQL Analysis in Notebook](https://mlsanity.com/paper/2201.05664/) (2022)
- [Mining Precision Interfaces From Query Logs](https://mlsanity.com/paper/1712.00078/) (2017)

## Built on by (most cited first, 8 of 8)

- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks](https://mlsanity.com/paper/2404.07387/) (2024)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization](https://mlsanity.com/paper/2502.11140/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions](https://mlsanity.com/paper/2406.15259/) (2024)
- [MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction](https://mlsanity.com/paper/2607.19910/) (2026)

## Abstract (arXiv)

We develop NL2INTERFACE to explore the potential of generating usable interactive multi-visualization interfaces from natural language queries. With NL2INTERFACE, users can directly write natural language queries to automatically generate a fully interactive multi-visualization interface without any extra effort of learning a tool or programming language. Further, users can interact with the interfaces to easily transform the data and quickly see the results in the visualizations.