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Document VQA performance of different models and prompting strategies on the [BoundingDocs v2.0 dataset](https://huggingface.co/datasets/letxbe/BoundingDocs). <br>
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The best value is shown in **bold**, the second-best value is <ins>underlined</ins>.
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## Limitations
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- **Prototype only**: Intended as a first approach, not a production-ready solution.
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- **Dataset constraints**: Current evaluation is limited to cases where an answer fits in a single bounding box. Answers requiring reasoning over multiple regions or not fully captured by OCR cannot be properly
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Document VQA performance of different models and prompting strategies on the [BoundingDocs v2.0 dataset](https://huggingface.co/datasets/letxbe/BoundingDocs). <br>
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The best value is shown in **bold**, the second-best value is <ins>underlined</ins>.
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## Citation
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If you use `DocExplainer`, please cite:
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```bibtex
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@misc{chen2025reliableinterpretabledocumentquestion,
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title={Towards Reliable and Interpretable Document Question Answering via VLMs},
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author={Alessio Chen and Simone Giovannini and Andrea Gemelli and Fabio Coppini and Simone Marinai},
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year={2025},
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eprint={2509.10129},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2509.10129},
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}
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```
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## Limitations
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- **Prototype only**: Intended as a first approach, not a production-ready solution.
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- **Dataset constraints**: Current evaluation is limited to cases where an answer fits in a single bounding box. Answers requiring reasoning over multiple regions or not fully captured by OCR cannot be properly
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