mlsanityData to 7 Oct 2026

TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

arXiv 2404.19205 ↗First posted 30 Apr 2024cs.CV cs.AIcited by 68

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Cited by 32 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. It is important to note that existing datasets have not incorporated images or QA pairs, which are two crucial components of TableVQA. As such, the primary objective of this paper is to obtain these necessary components. Specifically, images are sourced either through the application of a textitstylesheet or by employing the proposed table rendering system. QA pairs are generated by exploiting the large language model (LLM) where the input is a text-formatted table. Ultimately, the completed TableVQA-Bench comprises 1,500 QA pairs. We comprehensively compare the performance of various multi-modal large language models (MLLMs) on TableVQA-Bench. GPT-4V achieves the highest accuracy among commercial and open-sourced MLLMs from our experiments. Moreover, we discover that the number of vision queries plays a significant role in TableVQA performance. To further analyze the capabilities of MLLMs in comparison to their LLM backbones, we investigate by presenting image-formatted tables to MLLMs and text-formatted tables to LLMs, respectively. Our findings suggest that processing visual inputs is more challenging than text inputs, as evidenced by the lower performance of MLLMs, despite generally requiring higher computational costs than LLMs. The proposed TableVQA-Bench and evaluation codes are available at hrefhttps://github.com/naver-ai/tablevqabenchhttps://github.com/naver-ai/tablevqabench.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 25 of its 29 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

25 of 29 references.

References · 25

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  4. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  5. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  6. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  7. 2023MMBench: Is Your Multi-modal Model an All-around Player?cited by 2,105
  8. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  9. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  10. 2023SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehensioncited by 929
  11. 2023CogVLM: Visual Expert for Pretrained Language Modelscited by 704
  12. 2023CogAgent: A Visual Language Model for GUI Agentscited by 692
13 more
  1. 2023mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaborationcited by 545
  2. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  3. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  4. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 360
  5. 2023SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Modelscited by 283
  6. 2019Image-based table recognition: data, model, and evaluationcited by 233
  7. 2023MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuningcited by 149
  8. 2020Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Contextcited by 146
  9. 2023ChartBench: A Benchmark for Complex Visual Reasoning in Chartscited by 116
  10. 2023SEED-Bench-2: Benchmarking Multimodal Large Language Modelscited by 96
  11. 2022TableFormer: Table Structure Understanding with Transformerscited by 93
  12. 2023Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Modelscited by 4
  13. 2023SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain Gapcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 32; 2026: 29.

68 papers.

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  1. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 443
  2. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
  3. 2025VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Usecited by 84
  4. 2025ReFocus: Visual Editing as a Chain of Thought for Structured Image Understandingcited by 81
  5. 2025Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generationcited by 54
  6. 2025Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmarkcited by 41
  7. 2025Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scalingcited by 30
  8. 2025One RL to See Them All: Visual Triple Unified Reinforcement Learningcited by 29
  9. 2025Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluationcited by 28
  10. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  11. 2025WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?cited by 26
  12. 2024Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Modelcited by 21
38 more
  1. 2025M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performancecited by 19
  2. 2025Multimodal Tabular Reasoning with Privileged Structured Informationcited by 14
  3. 2025NegVQA: Can Vision Language Models Understand Negation?cited by 12
  4. 2025RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysiscited by 12
  5. 2026Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoningcited by 10
  6. 2026Imagination Helps Visual Reasoning, But Not Yet in Latent Spacecited by 10
  7. 2024Knowledge-Aware Reasoning over Multimodal Semi-structured Tablescited by 9
  8. 2025Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Reviewcited by 8
  9. 2025Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Modelscited by 8
  10. 2025DocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translationcited by 8
  11. 2025AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understandingcited by 7
  12. 2026Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmarkcited by 7
  13. 2024MageBench: Bridging Large Multimodal Models to Agentscited by 6
  14. 2025Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Imagescited by 6
  15. 2025HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimizationcited by 5
  16. 2025TABLET: A Large-Scale Dataset for Robust Visual Table Understandingcited by 5
  17. 2026Large Vision-Language Models Get Lost in Attentioncited by 5
  18. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  19. 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligencecited by 4
  20. 2026MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hintscited by 4
  21. 2025NoTeS-Bank: Benchmarking Neural Transcription and Search for Scientific Notes Understandingcited by 3
  22. 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Modelscited by 3
  23. 2026VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learningcited by 3
  24. 2024BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Datacited by 2
  25. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  26. 2025GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoningcited by 2
  27. 2025Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMscited by 2
  28. 2025TALENT: Table VQA via Augmented Language-Enhanced Natural-text Transcriptioncited by 2
  29. 2026How to Train Your Long-Context Visual Document Modelcited by 2
  30. 2026Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understandingcited by 2
  31. 2025Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Modelscited by 1
  32. 2025HCT-QA: A Benchmark for Question Answering on Human-Centric Tablescited by 1
  33. 2025Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?cited by 1
  34. 2025ExpliCIT-QA: Explainable Code-Based Image Table Question Answeringcited by 1
  35. 2025TabRAG: Improving Tabular Document Question Answering for Retrieval Augmented Generation via Structured Representationscited by 1
  36. 2026WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wildcited by 1
  37. 2026DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesiscited by 1
  38. 2026TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMscited by 1

50 most cited of 68 All 68 (JSON)

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  1. 2025MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space
  2. 2024A Comprehensive Survey on Visual Question Answering Datasets and Algorithms
  3. 2025Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering
  4. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
  5. 2025Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison
  6. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
  7. 2025ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
  8. 2023VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization