# nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task

arXiv: https://arxiv.org/abs/2112.12926 · HTML: https://mlsanity.com/paper/2112.12926/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [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)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations](https://mlsanity.com/paper/2110.00680/) (2021)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [Interweaving Multimodal Interaction with Flexible Unit Visualizations for Data Exploration](https://mlsanity.com/paper/2004.10428/) (2020)

## Built on by (most cited first, 28 of 28)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering](https://mlsanity.com/paper/2405.07001/) (2024)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code](https://mlsanity.com/paper/2412.02764/) (2024)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis](https://mlsanity.com/paper/2301.10385/) (2023)
- [FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models](https://mlsanity.com/paper/2401.02982/) (2024)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation](https://mlsanity.com/paper/2506.13326/) (2025)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [VisCoder2: Building Multi-Language Visualization Coding Agents](https://mlsanity.com/paper/2510.23642/) (2025)
- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents](https://mlsanity.com/paper/2506.08800/) (2025)
- [VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?](https://mlsanity.com/paper/2608.10408/) (2026)
- [Natural Language Models for Data Visualization Utilizing nvBench Dataset](https://mlsanity.com/paper/2310.00832/) (2023)
- [Multi-Agent Actor-Critic Generative AI for Query Resolution and Analysis](https://mlsanity.com/paper/2502.13164/) (2025)
- [Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization](https://mlsanity.com/paper/2601.04582/) (2026)
- [Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework](https://mlsanity.com/paper/2606.29808/) (2026)
- [Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics](https://mlsanity.com/paper/2603.05832/) (2026)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [GQVis: A Dataset of Genomics Data Questions and Visualizations for Generative AI](https://mlsanity.com/paper/2510.13816/) (2025)
- [CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation](https://mlsanity.com/paper/2512.19173/) (2025)
- [NOVAID: Natural-language Observability Visualization Assistant for ITOps Dashboard Widget Generation](https://mlsanity.com/paper/2601.11531/) (2025)
- [DashArena: Benchmarking LLMs on Interactive Analytic Dashboard Generation](https://mlsanity.com/paper/2608.10567/) (2026)
- [DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?](https://mlsanity.com/paper/2608.26757/) (2026)
- [Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?](https://mlsanity.com/paper/2609.26208/) (2026)

## Abstract (arXiv)

NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and academic researchers. In the last few years, the advanced deep learning-based models have achieved human-like abilities in many natural language processing (NLP) tasks, which clearly tells us that the deep learning-based technique is a good choice to push the field of NL2VIS. However, a big balk is the lack of benchmarks with lots of (NL, VIS) pairs. We present nvBench, the first large-scale NL2VIS benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NL2VIS task. The quality of nvBench has been extensively validated by 23 experts and 300+ crowd workers. Deep learning-based models training using nvBench demonstrate that nvBench can push the field of NL2VIS.