Datasets:

VillanovaAI
/

multi_figureQA

dataset_info:
  - config_name: de
    features:
      - name: image_index
        dtype: int64
      - name: image
        dtype: image
      - name: graph_type
        dtype: string
      - name: source_code
        dtype: string
      - name: persona
        dtype: string
      - name: question_type
        dtype: string
      - name: question
        dtype: string
      - name: answer_format
        dtype: string
      - name: answer
        dtype: string
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      - name: image_index
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      - name: image
        dtype: image
      - name: graph_type
        dtype: string
      - name: source_code
        dtype: string
      - name: persona
        dtype: string
      - name: question_type
        dtype: string
      - name: question
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      - name: answer
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    features:
      - name: image_index
        dtype: int64
      - name: image
        dtype: image
      - name: graph_type
        dtype: string
      - name: source_code
        dtype: string
      - name: persona
        dtype: string
      - name: question_type
        dtype: string
      - name: question
        dtype: string
      - name: answer_format
        dtype: string
      - name: answer
        dtype: string
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  - config_name: fr
    features:
      - name: image_index
        dtype: int64
      - name: image
        dtype: image
      - name: graph_type
        dtype: string
      - name: source_code
        dtype: string
      - name: persona
        dtype: string
      - name: question_type
        dtype: string
      - name: question
        dtype: string
      - name: answer_format
        dtype: string
      - name: answer
        dtype: string
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  - config_name: it
    features:
      - name: image_index
        dtype: int64
      - name: image
        dtype: image
      - name: graph_type
        dtype: string
      - name: source_code
        dtype: string
      - name: persona
        dtype: string
      - name: question_type
        dtype: string
      - name: question
        dtype: string
      - name: answer_format
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
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configs:
  - config_name: de
    data_files:
      - split: train
        path: de/train-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
  - config_name: es
    data_files:
      - split: train
        path: es/train-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
  - config_name: it
    data_files:
      - split: train
        path: it/train-*
license: odc-by
task_categories:
  - visual-question-answering
language:
  - fr
  - it
  - de
  - en
  - es
size_categories:
  - 100K<n<1M

multi_figureQA

Overview

Multi-FigureQA is visual question-answering (VQA) and reasoning dataset based on Microsoft's FigureQA dataset.

The original FigureQA dataset consists of synthetic, scientific-style figures (such as dot-line plots, line plots, horizontal and vertical bar graphs and pie charts) paired with source data and Q&A pairs.

While the original FigureQA dataset provides clean synthetic data for basic chart comprehension, its plots lack realistic labels, titles, and domain-specific context. As a result, the generated figures offer limited practical utility for contemporary visual reasoning tasks.

For more information about Microsoft's FigureQA VQA dataset check the realated paper on ArXiv.

To overcome the abstract nature of the original dataset, Multi-FigureQA introduces persona-driven figure generation. By using detailed persona descriptions, the pipeline generates domain-specific titles, descriptive labels, and realistic data distributions, significantly increasing the dataset's practical utility. Q&A pairs are then generated on the basis of the Persona and related figure.

In-prompt instructions ensure question ad topic diversity and strict answer format.

Questions and answers rely exclusively on what is visibly displayed in the chart and described in the source code.
The generation logic restricts questions when chart values are visually too close to differentiate, preventing unanswerable or ambiguous answers.
To prevent over-representation of simple extreme values, questions are evenly distributed across four distinct categories: highest_lowest,pairwise_comparison, relative_ranking, presence_composition.
Responses are structured into four constrained formats: yes_no,detailed_explanation, concise, label_only.

Dataset structure

{
    "image_index": int64, 
    "image": image, 
    "graph_type": string, 
    "source_code": string, 
    "persona": string, 
    "question_type": string
    "question": string,
    "answer_format": string
    "answer": string
}

Multilingual generation

The dataset contains approximately 200,000 samples, with 60% in English. The remaining 40% is distributed evenly among French, Spanish, German, and Italian (10% each). The final distribution is as follows:

Language	Subset Name	Percentage	Number of Examples
English	en	60%	119,920
Italian	it	10%	19,962
French	fr	10%	19,988
Spanish	es	10%	19,994
German	de	10%	19,948
Total		100%	199,812

Models

Figure and Q&A generation was achieved using the state-of-the-art, permissively licensed model Qwen/Qwen3.5-122B-A10B-FP8.

Licensing

The dataset is distributed under the ODC-BY-1.0 license. Users must adhere to the attribution and usage requirements outlined under this license.