# RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic

arXiv: https://arxiv.org/abs/2308.01979 · HTML: https://mlsanity.com/paper/2308.01979/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [VisualMRC: Machine Reading Comprehension on Document Images](https://mlsanity.com/paper/2101.11272/) (2021)
- [Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer](https://mlsanity.com/paper/2102.09550/) (2021)
- [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://mlsanity.com/paper/2212.09662/) (2022)
- [SelfDoc: Self-Supervised Document Representation Learning](https://mlsanity.com/paper/2106.03331/) (2021)
- [Unified Pretraining Framework for Document Understanding](https://mlsanity.com/paper/2204.10939/) (2022)
- [MapQA: A Dataset for Question Answering on Choropleth Maps](https://mlsanity.com/paper/2211.08545/) (2022)
- [Towards Complex Document Understanding By Discrete Reasoning](https://mlsanity.com/paper/2207.11871/) (2022)
- [LEAF-QA: Locate, Encode & Attend for Figure Question Answering](https://mlsanity.com/paper/1907.12861/) (2019)
- [MATE: Multi-view Attention for Table Transformer Efficiency](https://mlsanity.com/paper/2109.04312/) (2021)
- [HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving](https://mlsanity.com/paper/1703.00426/) (2017)
- [ICDAR 2021 Competition on Document VisualQuestion Answering](https://mlsanity.com/paper/2111.05547/) (2021)
- [Classification-Regression for Chart Comprehension](https://mlsanity.com/paper/2111.14792/) (2021)
- [Recent, rapid advancement in visual question answering architecture: a review](https://mlsanity.com/paper/2203.01322/) (2022)

## Built on by (most cited first, 8 of 8)

- [EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding](https://mlsanity.com/paper/2409.01577/) (2024)
- [Megrez-Omni Technical Report](https://mlsanity.com/paper/2502.15803/) (2025)
- [DocTabQA: Answering Questions from Long Documents Using Tables](https://mlsanity.com/paper/2408.11490/) (2024)
- [DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering](https://mlsanity.com/paper/2503.15887/) (2025)
- [ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats](https://mlsanity.com/paper/2606.01348/) (2026)
- [CHAOS: Chart Analysis with Outlier Samples](https://mlsanity.com/paper/2505.17235/) (2025)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis](https://mlsanity.com/paper/2610.14173/) (2026)

## Abstract (arXiv)

We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, 'list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models and advance the field of chart visual QA and formal logic verification for neural networks in general.