Cover Image

MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Zifeng, Jia, Mengzhao, Zhang, Zhihan, Li, Lang, Jiang, Meng
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks
by: Jia, Mengzhao, et al.
Published: (2024)

MSG-Chart: Multimodal Scene Graph for ChartQA
by: Dai, Yue, et al.
Published: (2024)

ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
by: Niu, Tianhao, et al.
Published: (2026)

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models
by: Jiang, Yifan, et al.
Published: (2026)

FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models
by: Shu, Dong, et al.
Published: (2025)

POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering
by: Xu, Yichen, et al.
Published: (2025)

ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
by: Li, Zhen, et al.
Published: (2025)

EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts
by: Mukherjee, Kushin, et al.
Published: (2025)

ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
by: Tang, Liyan, et al.
Published: (2025)

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)

CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
by: Wang, Zirui, et al.
Published: (2024)

Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
by: Laskar, Md Tahmid Rahman, et al.
Published: (2025)

ChartQA-X: Generating Explanations for Visual Chart Reasoning
by: Hegde, Shamanthak, et al.
Published: (2025)

ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
by: Wu, Yifan, et al.
Published: (2024)

ChartCheck: Explainable Fact-Checking over Real-World Chart Images
by: Akhtar, Mubashara, et al.
Published: (2023)

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
by: Xie, Tianchi, et al.
Published: (2025)

On the Perception Bottleneck of VLMs for Chart Understanding
by: Liu, Junteng, et al.
Published: (2025)

ChartCap: Mitigating Hallucination of Dense Chart Captioning
by: Lim, Junyoung, et al.
Published: (2025)

CHAOS: Chart Analysis with Outlier Samples
by: Moured, Omar, et al.
Published: (2025)

Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations
by: Ford, James, et al.
Published: (2024)

CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
by: Deng, Dazhen, et al.
Published: (2025)

RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
by: Vogel, Alexander, et al.
Published: (2025)

CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
by: Bharti, Shubham, et al.
Published: (2024)

Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature
by: Shen, Lingdong, et al.
Published: (2024)

ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
by: Yang, Cheng, et al.
Published: (2024)

ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
by: Xia, Renqiu, et al.
Published: (2024)

ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild
by: Masry, Ahmed, et al.
Published: (2024)

ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts
by: Su, Ruiran, et al.
Published: (2025)

PM4Bench: Benchmarking Large Vision-Language Models with Parallel Multilingual Multi-Modal Multi-task Corpus
by: Gao, Junyuan, et al.
Published: (2025)

Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
by: Huang, Kung-Hsiang, et al.
Published: (2025)

METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling
by: Li, Bingxuan, et al.
Published: (2025)

ChartAct: A Benchmark for Dynamic Chart Understanding
by: Huang, Muye, et al.
Published: (2026)

ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
by: Meng, Fanqing, et al.
Published: (2024)

mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning
by: Wei, Jingxuan, et al.
Published: (2024)

On Pre-training of Multimodal Language Models Customized for Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2024)

Effective Training Data Synthesis for Improving MLLM Chart Understanding
by: Yang, Yuwei, et al.
Published: (2025)

ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
by: Xu, Zhengzhuo, et al.
Published: (2024)

ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
by: Kondic, Jovana, et al.
Published: (2026)

Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
by: Efat, Azher Ahmed, et al.
Published: (2026)

VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering
by: Wang, Yanling, et al.
Published: (2025)