mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding

A curated list of recent and past chart understanding work based on our IEEE TKDE survey paper: From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models.

[NeurIPS 2024] CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

[ICLR2025 Oral] ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding

Code and data for the ACL 2024 Findings paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning"

[NeurIPS 2025] ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models

A curated collection of papers, datasets, and resources on the topic of misleading chart understanding

Code associated with the preprint: "Is this chart lying to me? Automating the detection of misleading visualization"

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Scientific Chart Understanding

This is the official repository for our paper 📄 “In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding”

SciVQA: Scientific Visual Question Answering shared task