README.md

metadata

dataset_info:
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    - name: id
      dtype: string
    - name: conversations
      list:
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        - name: content
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  splits:
    - name: train
      num_bytes: 277884785
      num_examples: 160000
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  dataset_size: 277884785
configs:
  - config_name: default
    data_files:
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        path: data/train-*

Text-Based Reasoning About Vector Graphics

🌐 Homepage • 📃 Paper • 🤗 Data (PVD-160k) • 🤗 Model (PVD-160k-Mistral-7b) • 💻 Code

We propose VDLM, a text-based visual reasoning framework for vector graphics. VDLM operates on text-based visual descriptions—specifically, SVG representations and learned Primal Visual Descriptions (PVD), enabling zero-shot reasoning with an off-the-shelf LLM. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our paper for more details.