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--- |
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dataset_info: |
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features: |
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- name: question_id |
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dtype: string |
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- name: qtype |
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dtype: string |
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- name: figure_path |
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dtype: image |
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- name: visual_figure_path |
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list: image |
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- name: question |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: instructions |
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dtype: string |
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- name: prompt |
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dtype: string |
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- name: options |
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list: string |
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splits: |
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|
- name: info |
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num_bytes: 9389294399.0 |
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num_examples: 55091 |
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|
- name: plain |
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|
num_bytes: 15950918129.0 |
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num_examples: 55091 |
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|
- name: visual_metaphor |
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|
num_bytes: 144053150.0 |
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num_examples: 450 |
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|
- name: visual_basic |
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|
num_bytes: 1254942699.466 |
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num_examples: 7297 |
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download_size: 20376840742 |
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dataset_size: 26739208377.466 |
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|
configs: |
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- config_name: default |
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data_files: |
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- split: info |
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path: data/info-* |
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- split: plain |
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path: data/plain-* |
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- split: visual_metaphor |
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path: data/visual_metaphor-* |
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- split: visual_basic |
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path: data/visual_basic-* |
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--- |
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# InfoChartQA: Benchmark for Multimodal Question Answering on Infographic Charts |
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🤗[Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA) |
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# Dataset |
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You can find our dataset on huggingface: 🤗[InfoChartQA Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA) |
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# Usage |
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Each question entry is arranged as: |
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``` |
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--question_id: int |
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--qtype: int |
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--figure_path: image |
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--visual_figure_path: list of image |
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--question: str |
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--answer: str |
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--instructions: str |
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--prompt: str |
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--options: list of dict ("A/B/C/D":"option_content") |
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``` |
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Each question is built as: |
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``` |
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image_input: figure_path, visual_figure_path_1...visual_figure_path_n (if any) |
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text_input: prompt (if any) + question + options (if any) + instructions (if any) |
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``` |
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# Evaluate |
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You should store and evaluate model's response as: |
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|
```python |
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# Example code for evaluate |
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def build_question(query):#to build the question |
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question = "" |
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if "prompt" in query: |
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question = question + f"{query["prompt"]}\n" |
|
|
question = question + f"{query["question"]}\n" |
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if "options" in query: |
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for _ in query["options"]: |
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question = question + f"{_} {query['options'][_]}\n" |
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if "instructions" in query: |
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question = question + query["instructions"] |
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return question |
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with open("visual_basic.json","r",encode="utf-8") as f: |
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queries = json.load(f) |
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for idx in range(queries): |
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|
question = build_question(queries[idx]) |
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|
figure_path = [queries[idx]['figure_path']] |
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|
visual_figure_path = queries[idx]['visual_figure_path'] |
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response = model.generate(question, [figure_path, visual_figure_path])# generate model's response based on |
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queries[idx]["response"] = reponse |
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with open("model_reponse.json","w",encode="utf-8") as f: |
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json.dump(queries, f) |
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from checker import evaluate |
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evaluate("model_reponse.json", "path_to_save_the_result") |
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``` |
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Or simply use after your answer is generated: |
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|
```python |
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python -c "import checker; checker.evaluate(sys.argv[1], sys.argv[2])" PATH_TO_INPUT_FILE PATH_TO_INPUT_FILE |
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``` |
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# LeaderBoard |
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|
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|
| Model | Infographic | Plain | Δ | Basic | Metaphor | Avg. | |
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|
| ------------------------ | ----------- | ------- | ----- | ------- | -------- | ----- | |
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| **Baselines** | | | | | | | |
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| Human | 95.35\* | 96.28\* | 0.93 | 93.17\* | 88.69 | 90.93 | |
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| **Proprietary Models** | | | | | | | |
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|
| OpenAI O4-mini | 79.41 | 94.61 | 15.20 | 92.12 | 54.76 | 73.44 | |
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| GPT-4o | 66.09 | 81.77 | 15.68 | 81.77 | 47.19 | 64.48 | |
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|
| Claude 3.5 Sonnet | 65.67 | 83.11 | 17.44 | 90.36 | 55.33 | 72.85 | |
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|
| Gemini 2.5 Pro Preview | 83.31 | 93.88 | 10.07 | 90.01 | 60.42 | 75.22 | |
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|
| Gemini 2.5 Flash Preview | 71.91 | 84.66 | 12.75 | 82.02 | 56.28 | 69.15 | |
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| **Open-Source Models** | | | | | | | |
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|
| Qwen2.5-VL-72B | 62.06 | 78.47 | 16.41 | 77.34 | 54.64 | 65.99 | |
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|
| Llama-4 Scout | 67.41 | 84.84 | 17.43 | 81.76 | 51.89 | 66.83 | |
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| Intern-VL3-78B | 66.38 | 82.18 | 15.80 | 79.46 | 51.52 | 65.49 | |
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|
| 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 | |
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|
| 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 | |
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|
| 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](https://github.com/princeton-nlp/CharXiv/blob/main/data/LICENSE) license. Our code is licensed under [Apache 2.0](https://github.com/princeton-nlp/CharXiv/blob/main/LICENSE) license. The copyright of the charts belong to the original authors. |
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## 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}, |
|
|
} |
|
|
``` |
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|
|
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|
