mlsanityData to 7 Oct 2026

ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

arXiv 2203.10244 ↗First posted 19 Mar 2022cs.CLcited by 1,449

On the map: LLM reasoning → Multimodal reasoning

Abstract (arXiv)

Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.

Key facts. Cited by 1,449 papers in the map (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 31 of its 43 references that are in the map. First posted on arXiv 19 Mar 2022.

Builds on

31 of 43 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  7. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  8. 2017Mask R-CNNcited by 7,259
  9. 2016Layer Normalizationcited by 5,579
  10. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  11. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  12. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
19 more
  1. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  2. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  3. 2021Unifying Vision-and-Language Tasks via Text Generationcited by 411
  4. 2018DVQA: Understanding Data Visualizations via Question Answeringcited by 338
  5. 2019Analysing Mathematical Reasoning Abilities of Neural Modelscited by 331
  6. 2019PlotQA: Reasoning over Scientific Plotscited by 317
  7. 2017FigureQA: An Annotated Figure Dataset for Visual Reasoningcited by 314
  8. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
  9. 2019What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysiscited by 191
  10. 2021FeTaQA: Free-form Table Question Answeringcited by 174
  11. 2020Injecting Numerical Reasoning Skills into Language Modelscited by 133
  12. 2016Graph-Structured Representations for Visual Question Answeringcited by 123
  13. 2020Understanding tables with intermediate pre-trainingcited by 68
  14. 2019LEAF-QA: Locate, Encode & Attend for Figure Question Answeringcited by 53
  15. 2019Answering Questions about Data Visualizations using Efficient Bimodal Fusioncited by 26
  16. 2019Data Extraction from Charts via Single Deep Neural Networkcited by 24
  17. 2021Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Chartscited by 12
  18. 2021Classification-Regression for Chart Comprehensioncited by 11
  19. 2018Adversarial TableQA: Attention Supervision for Question Answering on Tablescited by 10

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Citing papers per year: 2022: 7; 2023: 49; 2024: 304; 2025: 557; 2026: 532.

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  9. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  10. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,419
  11. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
  12. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,033
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  4. 2023CogAgent: A Visual Language Model for GUI Agentscited by 692
  5. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 653
  6. 2023HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Modelscited by 572
  7. 2024DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understandingcited by 559
  8. 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 502
  9. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 466
  10. 2025Qwen3-Omni Technical Reportcited by 451
  11. 2024LLaVA-CoT: Let Vision Language Models Reason Step-by-Stepcited by 446
  12. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 443
  13. 2024Mini-Gemini: Mining the Potential of Multi-modality Vision Language Modelscited by 361
  14. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 360
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  16. 2024LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Modelscited by 344
  17. 2024SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generationcited by 328
  18. 2024InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Modelcited by 326
  19. 2024What matters when building vision-language models?cited by 321
  20. 2024Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Modelscited by 321
  21. 2025R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimizationcited by 296
  22. 2024MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?cited by 284
  23. 2024Large Language Models for Mathematical Reasoning: Progresses and Challengescited by 283
  24. 2024PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reductioncited by 283
  25. 2023PaLI-X: On Scaling up a Multilingual Vision and Language Modelcited by 254
  26. 2025LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLcited by 244
  27. 2024Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimizationcited by 238
  28. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 237
  29. 2025Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcementcited by 234
  30. 2025LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Trainingcited by 233
  31. 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-trainingcited by 230
  32. 2025SmolVLM: Redefining small and efficient multimodal modelscited by 226
  33. 2024VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Taskscited by 222
  34. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
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  36. 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 212
  37. 2024NVILA: Efficient Frontier Visual Language Modelscited by 206
  38. 2024MetaMorph: Multimodal Understanding and Generation via Instruction Tuningcited by 205

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  4. 2025ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension
  5. 2026What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets
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  8. 2025Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data