# AnnoBench: A Benchmark for Visualization Annotation Generation

arXiv: https://arxiv.org/abs/2607.25911 · HTML: https://mlsanity.com/paper/2607.25911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 64 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 30 of 32)

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [Chart-to-Text: A Large-Scale Benchmark for Chart Summarization](https://mlsanity.com/paper/2203.06486/) (2022)
- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content](https://mlsanity.com/paper/2110.04406/) (2021)
- [ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing](https://mlsanity.com/paper/2505.11935/) (2025)
- [What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts](https://mlsanity.com/paper/2505.13360/) (2025)
- [nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task](https://mlsanity.com/paper/2112.12926/) (2021)
- [MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems](https://mlsanity.com/paper/2410.14179/) (2024)
- [Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way](https://mlsanity.com/paper/2408.16119/) (2024)
- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts](https://mlsanity.com/paper/2208.01780/) (2022)
- [Mini-VLAT: A Short and Effective Measure of Visualization Literacy](https://mlsanity.com/paper/2304.07905/) (2023)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text](https://mlsanity.com/paper/2507.19969/) (2025)
- [EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts](https://mlsanity.com/paper/2508.04650/) (2025)
- [ChartM3: Benchmarking Chart Editing with Multimodal Instructions](https://mlsanity.com/paper/2507.21167/) (2025)
- [ChartMark: A Structured Grammar for Chart Annotation](https://mlsanity.com/paper/2507.21810/) (2025)
- [Integrated Visualization Editing via Parameterized Declarative Templates](https://mlsanity.com/paper/2101.07902/) (2021)
- [Understanding Data Visualization Design Practice](https://mlsanity.com/paper/2108.07855/) (2021)
- [A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space](https://mlsanity.com/paper/2306.06043/) (2023)
- [The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions](https://mlsanity.com/paper/2401.04052/) (2024)
- [A Survey on Annotations in Information Visualization: Empirical Insights, Applications, and Challenges](https://mlsanity.com/paper/2410.05579/) (2024)
- [Understanding Reader Takeaways in Thematic Maps Under Varying Text, Detail, and Spatial Autocorrelation](https://mlsanity.com/paper/2403.08260/) (2024)
- [ReVISit 2: A Full Experiment Life Cycle User Study Framework](https://mlsanity.com/paper/2508.03876/) (2025)
- [Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective](https://mlsanity.com/paper/2302.00707/) (2023)

## Built on by (most cited first, 1 of 1)

- [ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation](https://mlsanity.com/paper/2608.03464/) (2026)

## Abstract (arXiv)

Annotation is among the most demanding visualization tasks to automate, as it simultaneously requires correctly navigating visual, semantic, and stylistic constraints. Failure to meet any of these conditions severely undermines the utility of an annotation, rendering it challenging to read, inaccurate, or visually discordant. Despite a growing body of annotation tools and automations, no existing benchmark or evaluation framework tests whether these conditions are met because of their scope and annotation not being the focus of their studies. We introduce AnnoBench, a benchmark for visualization annotation that materializes the inherent challenges of this domain in a structured and testable manner. AnnoBench pairs visualizations from professional data journalism and visualization galleries with annotation tasks, spanning four representation formats, five chart description conditions, and two prompt specification levels. The benchmark is executed via VLM-as-a-judge, using models aligned with manual human assessment. We evaluate the benchmark via four one-factor-at-a-time experiments, exploring the effects of input representation, semantic context, and prompt specificity, and model selection on annotation quality. This work provides a foundation for advancing annotation automation, tooling, and visualization-generation pipelines.