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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: conversations |
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list: |
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- name: role |
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dtype: string |
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- name: content |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 277884785 |
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num_examples: 160000 |
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download_size: 126665150 |
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dataset_size: 277884785 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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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="https://arxiv.org/abs/2404.06479">📃 Paper</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 visual reasoning framework that operates with intermediate text-based visual descriptions—SVG representations and learned Primal Visual Description, which can be directly integrated into existing LLMs and LMMs. 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](https://arxiv.org/abs/2404.06479) for more details. |
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