Evaluating Graphical Perception of Large Multimodal Models

Source code and data for the Graphical Perception Evaluation and Study: Zhang et al. 2025

Chart Reasoning (RQ1 and RQ2 in the paper)

To get started, copy openai-keys.env.template into a file named openai-keys.env and fill necessary environment variables (supports only AzureOpenAI keys at the moment). Azure OpenAI keys will be used by agents/client.py to initialize models.

Env Setup

conda create --prefix ./env/graphical_perception  python=3.9 -y

conda activate ./env/graphical_perception

pip install -r requirements.txt

Vis-text Data Downloading

cd data
chmod 777 download_data.sh
./download_data.sh --scenegraphs --vl_spec

OpenAI Key Setup

cp openai-keys.env.template openai-keys.env

then edit openai-keys.env with your own keys.

Run Chart Reasoning Pipeline

In labs/, run chart-reasoning-pipeline.ipynb to generate tasks, run GPT-4o/4v (determined by chart_reasoning/utils/clients.py), and get results evaluated by GPT-4o/4v and the detailed metrics. Note that table generation needs executable Chrome browser. To evaluate and get the final metrics for open-source models, please run chart-reasoning-oss-pipeline.ipynb

Chart Probing (RQ3 in the paper)

Please refer to chart_probing/README.md for more details.

Questions or Bugs?

If you have any questions, please feel free to contact drogozhang[AT]gmail[DOT]com or open an issue so we can help you better and quicker :)

Contributing

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