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dataset_info: |
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- name: conversations |
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- name: content |
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splits: |
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num_examples: 160000 |
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dataset_size: 277884785 |
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configs: |
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data_files: |
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path: data/train-* |
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<h1 align="center"> Text-Based Reasoning About Vector Graphics </h1> |
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<p align="center"> |
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<a href="https://mikewangwzhl.github.io/VDLM">🌐 Homepage</a> |
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<a href="">📃 Paper (Coming Soon)</a> |
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<a href="https://huggingface.co/datasets/mikewang/PVD-160K" >🤗 Data (PVD-160k)</a> |
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<a href="https://huggingface.co/mikewang/PVD-160k-Mistral-7b" >🤗 Model (PVD-160k-Mistral-7b)</a> |
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<a href="https://github.com/MikeWangWZHL/VDLM" >💻 Code</a> |
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</p> |
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We observe that current *large multimodal models (LMMs)* still struggle with seemingly straightforward reasoning tasks that require precise perception of low-level visual details, such as identifying spatial relations or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics—images composed purely of 2D objects and shapes. |
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To solve this challenge, we propose **Visually Descriptive Language Model (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 (coming soon)]() for more details. |
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