# ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation

arXiv: https://arxiv.org/abs/2608.03464 · HTML: https://mlsanity.com/paper/2608.03464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 30 of 35)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://mlsanity.com/paper/2406.18521/) (2024)
- [ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation](https://mlsanity.com/paper/2406.09961/) (2024)
- [ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering](https://mlsanity.com/paper/2504.05506/) (2025)
- [MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization](https://mlsanity.com/paper/2402.11453/) (2024)
- [ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation](https://mlsanity.com/paper/2501.06598/) (2025)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling](https://mlsanity.com/paper/2502.17651/) (2025)
- [ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing](https://mlsanity.com/paper/2505.11935/) (2025)
- [DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts](https://mlsanity.com/paper/2408.05346/) (2024)
- [MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems](https://mlsanity.com/paper/2410.14179/) (2024)
- [PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback](https://mlsanity.com/paper/2502.00988/) (2025)
- [nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning](https://mlsanity.com/paper/2503.12880/) (2025)
- [Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts](https://mlsanity.com/paper/2208.01780/) (2022)
- [InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts](https://mlsanity.com/paper/2505.19028/) (2025)
- [From Charts to Code: A Hierarchical Benchmark for Multimodal Models](https://mlsanity.com/paper/2510.17932/) (2025)
- [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)
- [ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing](https://mlsanity.com/paper/2511.15266/) (2025)
- [Charts Are Not Images: On the Challenges of Scientific Chart Editing](https://mlsanity.com/paper/2512.00752/) (2025)
- [ChartM3: Benchmarking Chart Editing with Multimodal Instructions](https://mlsanity.com/paper/2507.21167/) (2025)
- [ChartEditBench: Evaluating Grounded Multi-Turn Chart Editing in Multimodal Language Models](https://mlsanity.com/paper/2602.15758/) (2026)
- [C2: Scalable Auto-Feedback for LLM-based Chart Generation](https://mlsanity.com/paper/2410.18652/) (2024)
- [ChartMark: A Structured Grammar for Chart Annotation](https://mlsanity.com/paper/2507.21810/) (2025)
- [ChartE3: A Comprehensive Benchmark for End-to-End Chart Editing](https://mlsanity.com/paper/2601.21694/) (2026)
- [MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction](https://mlsanity.com/paper/2604.01600/) (2026)
- [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)

## Abstract (arXiv)

Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer new opportunities for automatic chart annotation authoring, their capabilities in this task remain underexplored. To address this gap, we introduce ChartAnno, a comprehensive benchmark for evaluating MLLMs on chart annotation generation. ChartAnno contains 1,200 real-world charts with paired annotated and unannotated executable code, along with 3,600 annotation instructions spanning three levels of specificity. We also develop a multidimensional evaluation framework combining rule-based and LLM-judged metrics to assess execution, structural compliance, semantic consistency, and design effectiveness. We evaluate 10 representative MLLMs under two primary chart input settings: (1) chart code alone and (2) both code and chart image. Results reveal that proprietary models lead overall, though open-source models narrow the gap. While higher instruction specificity improves annotation quality, inferring abstract communicative intent remains difficult across all models. Providing chart images yields marginal benefit when code is available. We also examine the effect of chart code through an image-only ablation and analyze the effects of multiple task complexity indicators and instruction-level transitions. Further analyses characterize common failure modes and validate the reliability of the LLM-based judge. Experiments with D3 and SVG demonstrate the generalizability of ChartAnno beyond its primary Python setting.