# Chart Question Answering from Real-World Analytical Narratives

arXiv: https://arxiv.org/abs/2507.01627 · HTML: https://mlsanity.com/paper/2507.01627/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://mlsanity.com/paper/2406.18521/) (2024)
- [All You May Need for VQA are Image Captions](https://mlsanity.com/paper/2205.01883/) (2022)
- [LEAF-QA: Locate, Encode & Attend for Figure Question Answering](https://mlsanity.com/paper/1907.12861/) (2019)
- [MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems](https://mlsanity.com/paper/2410.14179/) (2024)

## Built on by (most cited first, 3 of 3)

- [ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats](https://mlsanity.com/paper/2606.01348/) (2026)
- [Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts](https://mlsanity.com/paper/2604.21344/) (2026)
- [Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs](https://mlsanity.com/paper/2607.16105/) (2026)

## Abstract (arXiv)

We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives. Unlike prior benchmarks, our data reflects ecologically valid reasoning workflows. Benchmarking state-of-the-art multimodal large language models reveals a significant performance gap, with GPT-4.1 achieving an accuracy of 69.3%, underscoring the challenges posed by this more authentic CQA setting.