mlsanityData to 7 Oct 2026

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

arXiv 2105.14517 ↗First posted 30 May 2021cs.AIcited by 224

Not on the map. Why?

Cited by 111 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly dependent on handcraft rules and were merely evaluated on small-scale datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4,998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems. Compared with another publicly available dataset GeoS, GeoQA is 25 times larger, in which the program annotations can provide a practical testbed for future research on explicit and explainable numerical reasoning. Moreover, we introduce a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs. We further add multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic representation. Extensive experiments on GeoQA validate the effectiveness of our proposed NGS and auxiliary tasks. However, the results are still significantly lower than human performance, which leaves large room for future research. Our benchmark and code are released at https://github.com/chen-judge/GeoQA .

Key facts. Cited by 224 papers in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

21 of 41 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  6. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  8. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  9. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  10. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  11. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  12. 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 653
9 more
  1. 2019Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-trainingcited by 451
  2. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  3. 2017A simple neural network module for relational reasoningcited by 345
  4. 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 338
  5. 2018Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understandingcited by 304
  6. 2017Multi-task Self-Supervised Visual Learningcited by 200
  7. 2020How Useful is Self-Supervised Pretraining for Visual Tasks?cited by 80
  8. 2018Translating a Math Word Problem to an Expression Treecited by 57
  9. 2018Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognitioncited by 40

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 15; 2024: 58; 2025: 97; 2026: 46.

224 papers.

Sort:

By area:

  1. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
  2. 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,099
  3. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
  4. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 653
  5. 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?cited by 573
  6. 2024Large Language Models for Mathematical Reasoning: Progresses and Challengescited by 283
  7. 2024We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?cited by 281
  8. 2025MMaDA: Multimodal Large Diffusion Language Modelscited by 241
  9. 2023G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Modelcited by 208
  10. 2024NVILA: Efficient Frontier Visual Language Modelscited by 206
  11. 2023TheoremQA: A Theorem-driven Question Answering datasetcited by 189
  12. 2024Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Searchcited by 171
38 more
  1. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 150
  2. 2025GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoningcited by 147
  3. 2025SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvementcited by 144
  4. 2024DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Modelscited by 143
  5. 2022UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expressioncited by 137
  6. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  7. 2024MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scalecited by 125
  8. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  9. 2025OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cyclescited by 122
  10. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  11. 2024MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Enginecited by 111
  12. 2023GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoningcited by 107
  13. 2023Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuningcited by 106
  14. 2024Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Wantcited by 98
  15. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  16. 2025Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Modelscited by 92
  17. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 89
  18. 2025LaViDa: A Large Diffusion Language Model for Multimodal Understandingcited by 81
  19. 2024Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Modelscited by 75
  20. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  21. 2025InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Modelcited by 73
  22. 2025Virgo: A Preliminary Exploration on Reproducing o1-like MLLMcited by 68
  23. 2024Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Datacited by 67
  24. 2024Vision-Language Models Can Self-Improve Reasoning via Reflectioncited by 66
  25. 2025Unlocking Multimodal Mathematical Reasoning via Process Reward Modelcited by 64
  26. 2025RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?cited by 60
  27. 2023A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagramcited by 55
  28. 2024A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challengescited by 55
  29. 2024Reconstructive Visual Instruction Tuningcited by 54
  30. 2025SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learningcited by 53
  31. 2025Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learningcited by 51
  32. 2024Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklistcited by 48
  33. 2024AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?cited by 46
  34. 2025STORM: Token-Efficient Long Video Understanding for Multimodal LLMscited by 46
  35. 2025FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocitiescited by 46
  36. 2025UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Modelscited by 45
  37. 2021MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solverscited by 39
  38. 2021Neural-Symbolic Solver for Math Word Problems with Auxiliary Taskscited by 38

50 most cited of 224 All 224 (JSON)