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README.md
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ViDoSeek sets itself apart with its heightened difficulty level, attributed to the multi-document context and the intricate nature of its content types, particularly the Layout category. The dataset contains both single-hop and multi-hop queries, presenting a diverse set of challenges.
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We have also released the SlideVQA dataset, refined through our pipeline, which we refer to as SlideVQA-Refined. This dataset is suitable for evaluating retrieval-augmented generation tasks as well.
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The annotation is in the form of a JSON file.
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```json
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{
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```
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If you find this dataset useful, please consider citing our paper:
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```bigquery
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@misc{wang2025vidoragvisualdocumentretrievalaugmented,
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## 🚀Overview
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This is the Repo for ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus. The paper is available at [https://arxiv.org/abs/2502.18017](https://arxiv.org/abs/2502.18017).
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**ViDoSeek** sets itself apart with its heightened difficulty level, attributed to the multi-document context and the intricate nature of its content types, particularly the Layout category. The dataset contains both single-hop and multi-hop queries, presenting a diverse set of challenges.
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We have also released the **SlideVQA-Refined** dataset which is refined through our pipeline. This dataset is suitable for evaluating retrieval-augmented generation tasks as well.
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## 🔍Dataset Format
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The annotation is in the form of a JSON file.
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```json
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{
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}
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```
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## 📝 Citation
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If you find this dataset useful, please consider citing our paper:
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```bigquery
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@misc{wang2025vidoragvisualdocumentretrievalaugmented,
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