# ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding

arXiv: https://arxiv.org/abs/2409.03277 · HTML: https://mlsanity.com/paper/2409.03277/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Placed in MoE large language models, one of 12 directions in Mixture-of-experts.
- Builds on 55 of its 66 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Placed in

- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Builds on (most cited first, 30 of 55)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://mlsanity.com/paper/2310.09478/) (2023)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [ST-MoE: Designing Stable and Transferable Sparse Expert Models](https://mlsanity.com/paper/2202.08906/) (2022)
- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)

## Built on by (most cited first, 30 of 46)

- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)
- [Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner](https://mlsanity.com/paper/2507.15509/) (2025)
- [Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation](https://mlsanity.com/paper/2508.13587/) (2025)
- [HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better](https://mlsanity.com/paper/2607.04884/) (2026)
- [PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images](https://mlsanity.com/paper/2509.25185/) (2025)
- [ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing](https://mlsanity.com/paper/2511.15266/) (2025)
- [Shadow-FT: Tuning Instruct Model via Training on Paired Base Model](https://mlsanity.com/paper/2505.12716/) (2025)
- [VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction](https://mlsanity.com/paper/2511.23386/) (2025)
- [Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience](https://mlsanity.com/paper/2506.00842/) (2025)
- [UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis](https://mlsanity.com/paper/2507.00992/) (2025)
- [TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding](https://mlsanity.com/paper/2506.21393/) (2025)
- [SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More](https://mlsanity.com/paper/2601.05688/) (2026)
- [ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning](https://mlsanity.com/paper/2512.00305/) (2025)
- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding](https://mlsanity.com/paper/2505.15046/) (2025)
- [Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models](https://mlsanity.com/paper/2409.05381/) (2024)
- [Visual Programmability: A Guide for Code-as-Thought in Chart Understanding](https://mlsanity.com/paper/2509.09286/) (2025)
- [START: Spatial and Textual Learning for Chart Understanding](https://mlsanity.com/paper/2512.07186/) (2025)
- [ChartAgent: A Chart Understanding Framework with Tool Integrated Reasoning](https://mlsanity.com/paper/2512.14040/) (2025)
- [Infinity-Parser2 Technical Report](https://mlsanity.com/paper/2607.07836/) (2026)
- [Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation](https://mlsanity.com/paper/2505.23926/) (2025)
- [In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding](https://mlsanity.com/paper/2507.14298/) (2025)
- [Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data](https://mlsanity.com/paper/2503.16260/) (2025)
- [ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts](https://mlsanity.com/paper/2603.28902/) (2026)
- [Aligned Multi-View Scripts for Universal Chart-to-Code Generation](https://mlsanity.com/paper/2604.24559/) (2026)
- [ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats](https://mlsanity.com/paper/2606.01348/) (2026)
- [ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement](https://mlsanity.com/paper/2606.10640/) (2026)
- [Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework](https://mlsanity.com/paper/2606.29808/) (2026)
- [IE-Critic-R1: Advancing the Explanatory Measurement of Text-Driven Image Editing for Human Perception Alignment](https://mlsanity.com/paper/2511.18055/) (2025)

## Abstract (arXiv)

Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. However, current MLLMs still struggle to provide faithful data and reliable analysis only based on charts. To address it, we propose ChartMoE, which employs the Mixture of Expert (MoE) architecture to replace the traditional linear projector to bridge the modality gap. Specifically, we train several linear connectors through distinct alignment tasks, which are utilized as the foundational initialization parameters for different experts. Additionally, we introduce ChartMoE-Align, a dataset with nearly 1 million chart-table-JSON-code quadruples to conduct three alignment tasks (chart-table/JSON/code). Combined with the vanilla connector, we initialize different experts diversely and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters. Extensive experiments demonstrate the effectiveness of the MoE connector and our initialization strategy, e.g., ChartMoE improves the accuracy of the previous state-of-the-art from 80.48% to 84.64% on the ChartQA benchmark.