mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
[NeurIPS 2024] CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
[NeurIPS 2025] ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Scientific Chart Understanding
Code associated with the preprint: "Is this chart lying to me? Automating the detection of misleading visualization"
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
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