Datasets › PlotQA
PlotQA
PlotQA is a VQA dataset with 28.9 million question-answer pairs grounded over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates. Existing synthetic datasets (FigureQA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes either from a small fixed size vocabulary or from a bounding box within the image. However, in practice this is an unrealistic assumption because many questions require reasoning and thus have real valued answers which appear neither in a small fixed size vocabulary nor in the image. In this work, we aim to bridge this gap between existing datasets and real world plots by introducing PlotQA. Further, 80.76% of the out-of-vocabulary (OOV) questions in PlotQA have answers that are not in a fixed vocabulary.
Benchmarks archive 2025-07-28
All 5 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Chart Question Answering | PlotQA | MatCha4096 + LaMenDa 1:1 Accuracy 92.89 | Synthesize Step-by-Step: Tools Templates and LLMs as... | — | 6 | Compare |
| Visual Question Answering (VQA) | PlotQA-D1 | MatCha4096 + LaMenDa 1:1 Accuracy 93.94 | Synthesize Step-by-Step: Tools Templates and LLMs as... | — | 4 | Compare |
| Visual Question Answering (VQA) | PlotQA-D2 | MatCha4096 + LaMenDa 1:1 Accuracy 91.84 | Synthesize Step-by-Step: Tools Templates and LLMs as... | — | 4 | Compare |
| Visual Question Answering | PlotQA-D1 | MatCha4096 + LaMenDa 1:1 Accuracy 93.94 | Synthesize Step-by-Step: Tools Templates and LLMs as... | — | 2 | Compare |
| Visual Question Answering | PlotQA-D2 | MatCha4096 + LaMenDa 1:1 Accuracy 91.84 | Synthesize Step-by-Step: Tools Templates and LLMs as... | — | 2 | Compare |
Papers archive 2025-07-28
7 shown of 7 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 50. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Synthesize Step-by-Step: Tools Templates and LLMs as Data Generators for Reasoning-Based Chart VQA | 0 | 5 | 1 Jan 2024 | not harvested |
| DePlot: One-shot visual language reasoning by plot-to-table translation | 1 | 1 | 20 Dec 2022 | not harvested |
| MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering | 1 | 3 | 19 Dec 2022 | not harvested |
| ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning | 1 | 2 | 19 Mar 2022 | not harvested |
| Classification-Regression for Chart Comprehension | 1 | 3 | 29 Nov 2021 | ran 1 of 1 samples (0 unverified) |
| PlotQA: Reasoning over Scientific Plots | 0 | 2 | 3 Sep 2019 | not harvested |
| Answering Questions about Data Visualizations using Efficient Bimodal Fusion | 1 | 2 | 5 Aug 2019 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- PlotQA
- PlotQA-D1
- PlotQA-D2
3 variant names, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections