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@@ -181,6 +181,22 @@ Example Output:
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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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+
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+ If you use `DocExplainer`, please cite:
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+
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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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+
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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