metadata

dataset_info:
  features:
    - name: question_id
      dtype: string
    - name: qtype
      dtype: string
    - name: figure_path
      dtype: image
    - name: visual_figure_path
      list: image
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: instructions
      dtype: string
    - name: prompt
      dtype: string
    - name: options
      list: string
  splits:
    - name: info
      num_bytes: 9389294399
      num_examples: 55091
    - name: plain
      num_bytes: 15950918129
      num_examples: 55091
    - name: visual_metaphor
      num_bytes: 144053150
      num_examples: 450
    - name: visual_basic
      num_bytes: 1254942699.466
      num_examples: 7297
  download_size: 20376840742
  dataset_size: 26739208377.466
configs:
  - config_name: default
    data_files:
      - split: info
        path: data/info-*
      - split: plain
        path: data/plain-*
      - split: visual_metaphor
        path: data/visual_metaphor-*
      - split: visual_basic
        path: data/visual_basic-*
task_categories:
  - table-question-answering
language:
  - en

InfoChartQA: Benchmark for Multimodal Question Answering on Infographic Charts

Paper

🤗Dataset

Dataset

You can find our dataset on huggingface: 🤗InfoChartQA Dataset

Usage

Each question entry is arranged as:

--question_id: int
--qtype: int
--figure_path: image
--visual_figure_path: list of image
--question: str
--answer: str
--instructions: str
--prompt: str
--options: list of dict ("A/B/C/D":"option_content")

Each question is built as:

image_input: figure_path, visual_figure_path_1...visual_figure_path_n (if any)
text_iunput: prompt (if any) + question + options (if any) + instructions (if any)

Evaluate

You should store and evaluate model's response as:

# Example code for evaluate
def build_question(query):#to build the question
    question = ""
    if "prompt" in query:
        question = question + f"{query["prompt"]}
"
    question = question + f"{query["question"]}
"
    if "options" in query:
        for _ in query["options"]:
            question = question + f"{_} {query['options'][_]}
"
    if "instructions" in query:
        question = question + query["instructions"]
    return question

with open("visual_basic.json","r",encode="utf-8") as f:
    queries = json.load(f)

for idx in range(queries):
    question = build_question(queries[idx])
    figure_path = [queries[idx]['figure_path']]
    visual_figure_path = queries[idx]['visual_figure_path']
    
    response = model.generate(question, [figure_path, visual_figure_path])# generate model's response based on 
    
    queries[idx]["response"] = reponse

with open("model_reponse.json","w",encode="utf-8") as f:
    json.dump(queries, f)
from checker import evaluate
evaluate("model_reponse.json", "path_to_save_the_result")

Or simply use after your answer is generated:

python -c "import checker; checker.evaluate(sys.argv[1], sys.argv[2])" PATH_TO_INPUT_FILE PATH_TO_INPUT_FILE

LeaderBoard

Model	Infographic	Plain	Δ	Basic	Metaphor	Avg.
Baselines
Human	95.35*	96.28*	0.93	93.17*	88.69	90.93
Proprietary Models
OpenAI O4-mini	79.41	94.61	15.20	92.12	54.76	73.44
GPT-4o	66.09	81.77	15.68	81.77	47.19	64.48
Claude 3.5 Sonnet	65.67	83.11	17.44	90.36	55.33	72.85
Gemini 2.5 Pro Preview	83.31	93.88	10.07	90.01	60.42	75.22
Gemini 2.5 Flash Preview	71.91	84.66	12.75	82.02	56.28	69.15
Open-Source Models
Qwen2.5-VL-72B	62.06	78.47	16.41	77.34	54.64	65.99
Llama-4 Scout	67.41	84.84	17.43	81.76	51.89	66.83
Intern-VL3-78B	66.38	82.18	15.80	79.46	51.52	65.49
Intern-VL3-8B	56.82	73.50	16.68	74.26	49.57	61.92
Janus Pro	29.61	45.29	15.68	41.18	42.21	41.69
DeepSeek VL2	39.81	47.01	7.20	58.72	44.54	51.63
Phi-4	46.20	66.97	20.77	61.87	38.31	50.09
LLaVA OneVision Chat 78B	47.78	63.66	15.88	62.11	50.22	56.17
LLaVA OneVision Chat 7B	38.41	54.43	16.02	61.03	45.67	53.35
Pixtral	44.70	60.88	16.11	64.23	50.87	57.55
Ovis1.6-Gemma2-9B	50.56	64.52	13.98	60.96	34.42	47.69
ChartGemma	19.99	33.81	13.82	30.52	33.77	32.15
TinyChart	26.34	44.73	18.39	14.72	9.03	11.88
ChartInstruct-LLama2	20.55	27.91	7.36	33.86	33.12	33.49

License

Our original data contributions (all data except the charts) are distributed under the CC BY-SA 4.0 license. Our code is licensed under Apache 2.0 license. The copyright of the charts belong to the original authors.

Paper Links

📌 Main Paper (This Repository)

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
Minzhi Lin, Tianchi Xie, Mengchen Liu, Yilin Ye, Changjian Chen, Shixia Liu

Relevant Papers

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics Jiangning Zhu, Yuxing Zhou, Zheng Wang, Juntao Yao, Yima Gu, Yuhui Yuan, Shixia Liu_
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
Zhen Li, Duan Li, Yukai Guo, Xinyuan Guo, Bowen Li, Lanxi Xiao, Shenyu Qiao, Jiashu Chen, Zijian Wu, Hui Zhang, Xinhuan Shu, Shixia Liu

Cite

If you use our work and are inspired by our work, please consider cite us (available soon):

@misc{lin2025infochartqabenchmarkmultimodalquestion,
      title={InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts}, 
      author={Minzhi Lin and Tianchi Xie and Mengchen Liu and Yilin Ye and Changjian Chen and Shixia Liu},
      year={2025},
      eprint={2505.19028},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.19028}, 
}