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---
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: 9389294399.0
num_examples: 55091
- name: plain
num_bytes: 15950918129.0
num_examples: 55091
- name: visual_metaphor
num_bytes: 144053150.0
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-*
---
# InfoChartQA: Benchmark for Multimodal Question Answering on Infographic Charts
🤗[Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
# Dataset
You can find our dataset on huggingface: 🤗[InfoChartQA Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
# 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_input: prompt (if any) + question + options (if any) + instructions (if any)
```
# Evaluate
You should store and evaluate model's response as:
```python
# Example code for evaluate
def build_question(query):#to build the question
question = ""
if "prompt" in query:
question = question + f"{query["prompt"]}\n"
question = question + f"{query["question"]}\n"
if "options" in query:
for _ in query["options"]:
question = question + f"{_} {query['options'][_]}\n"
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
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](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.
## 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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