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ChartQA

Introduced by Ahmed Masry et al. in ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning19 Mar 2022 archive 2025-07-28

Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.

Benchmarks archive 2025-07-28

All 1 leaderboard 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)PaperCode
Chart Question Answering ChartQA ChartPaLI-5B + PaLM 2-S 1:1 Accuracy 81.3 Chart-based Reasoning: Transferring Capabilities from... — 27 Compare

Papers archive 2025-07-28

14 shown of 14 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 278. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs 0 2 19 Mar 2024 not harvested
ScreenAI: A Vision-Language Model for UI and Infographics Understanding 2 1 7 Feb 2024 not harvested
Synthesize Step-by-Step: Tools Templates and LLMs as Data Generators for Reasoning-Based Chart VQA 0 1 1 Jan 2024 not harvested
Gemini: A Family of Highly Capable Multimodal Models 1 1 19 Dec 2023 not harvested
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts 0 2 1 Dec 2023 not harvested
PaLI-3 Vision Language Models: Smaller, Faster, Stronger 1 2 13 Oct 2023 ran 3 of 3 samples (0 unverified)
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding 3 2 20 Sep 2023 not harvested
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond 2 2 24 Aug 2023 ran 0 of 2 samples (2 unverified; 2 pointer-only for licence)
PaLI-X: On Scaling up a Multilingual Vision and Language Model 2 3 29 May 2023 ran 6 of 7 samples (1 unverified)
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning 1 1 24 May 2023 ran 1 of 1 samples (0 unverified)
DePlot: One-shot visual language reasoning by plot-to-table translation 1 6 20 Dec 2022 not harvested
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering 1 1 19 Dec 2022 not harvested
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding 4 2 7 Oct 2022 ran 0 of 5 samples (5 unverified)
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning 1 1 19 Mar 2022 not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • ChartQA

1 variant name, as the archive lists them.

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