# ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing

arXiv: https://arxiv.org/abs/2505.11935 · HTML: https://mlsanity.com/paper/2505.11935/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 17 May 2025.

## 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, 25 of 25)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://mlsanity.com/paper/2406.18521/) (2024)
- [DePlot: One-shot visual language reasoning by plot-to-table translation](https://mlsanity.com/paper/2212.10505/) (2022)
- [ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation](https://mlsanity.com/paper/2406.09961/) (2024)
- [ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning](https://mlsanity.com/paper/2402.12185/) (2024)
- [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)
- [RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis](https://mlsanity.com/paper/2402.16117/) (2024)
- [Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback](https://mlsanity.com/paper/2410.04064/) (2024)
- [ChartReformer: Natural Language-Driven Chart Image Editing](https://mlsanity.com/paper/2403.00209/) (2024)

## Built on by (most cited first, 30 of 32)

- [ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM](https://mlsanity.com/paper/2506.14766/) (2025)
- [Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner](https://mlsanity.com/paper/2507.15509/) (2025)
- [VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning](https://mlsanity.com/paper/2511.00391/) (2025)
- [Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation](https://mlsanity.com/paper/2508.13587/) (2025)
- [Enhancing Multimodal Continual Instruction Tuning with BranchLoRA](https://mlsanity.com/paper/2506.02041/) (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)
- [JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence](https://mlsanity.com/paper/2510.23538/) (2025)
- [Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning](https://mlsanity.com/paper/2604.06079/) (2026)
- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement](https://mlsanity.com/paper/2512.13303/) (2025)
- [ChartE3: A Comprehensive Benchmark for End-to-End Chart Editing](https://mlsanity.com/paper/2601.21694/) (2026)
- [TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning](https://mlsanity.com/paper/2601.16520/) (2026)
- [OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward](https://mlsanity.com/paper/2604.05514/) (2026)
- [ChartSync: A Benchmark for Visuo-Logical Cascading Chart Editing](https://mlsanity.com/paper/2607.10301/) (2026)
- [Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs](https://mlsanity.com/paper/2507.06999/) (2025)
- [Octopus: History-Free Gradient Orthogonalization for Continual Learning in Multimodal Large Language Models](https://mlsanity.com/paper/2605.14938/) (2026)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ](https://mlsanity.com/paper/2608.13441/) (2026)
- [EdiTikZ: Scientific Figure Editing from Revision Trajectories](https://mlsanity.com/paper/2609.01409/) (2026)
- [Information-Theoretic Complementary Prompts for Improved Continual Text Classification](https://mlsanity.com/paper/2505.20933/) (2025)
- [ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation](https://mlsanity.com/paper/2608.03464/) (2026)
- [Who Leads Now? Token-Level Modality Arbitration for Chart-to-Code Generation](https://mlsanity.com/paper/2608.15510/) (2026)
- [REChart: Reasoning-Efficient Chart Editing with Large Reasoning Models](https://mlsanity.com/paper/2608.17414/) (2026)
- [Code as Representation: A Compilable Parsing Paradigm for Academic Documents](https://mlsanity.com/paper/2608.17550/) (2026)
- [Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing](https://mlsanity.com/paper/2609.08657/) (2026)
- [Hyper-LLaVA: Hyperbolic Uncertainty-aware Modality-Balanced Routing for Multimodal Continual Instruction Tuning](https://mlsanity.com/paper/2609.13742/) (2026)
- [Point, Revise, Review: Grounded Agentic Analysis in Reactive Notebooks with marimo-lens](https://mlsanity.com/paper/2609.19839/) (2026)
- [ChartRevise: A Dataset and Evaluation Protocol for Exact Chart Editing via Code](https://mlsanity.com/paper/2609.38642/) (2026)
- [VisAudit: Evaluating Multimodal Agents for Visual Diagnosis and Repair](https://mlsanity.com/paper/2610.02399/) (2026)

## Abstract (arXiv)

Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to integrate chart understanding and reasoning capacities, which are labor-intensive. While many MLLMs claim such editing capabilities, current evaluations rely on limited case studies, highlighting the urgent need for a comprehensive evaluation framework. In this work, we propose textscChartEdit, a novel benchmark designed for chart editing tasks, featuring 1405 diverse editing instructions applied to 233 real-world charts, each manually annotated and validated for accuracy. Utilizing textscChartEdit, we evaluate the performance of 10 mainstream MLLMs across two types of experiments at both the code and chart levels. The results suggest that large-scale models can generate code to produce images that partially match the reference images. However, their ability to generate accurate edits according to the instructions remains limited. The state-of-the-art (SOTA) model achieves a score of only 59.96, highlighting significant challenges in precise modification. In contrast, small-scale models, including chart-domain models, struggle both with following editing instructions and generating overall chart images, underscoring the need for further development in this area. Code is available at https://github.com/xxlllz/ChartEdit.