Towards Reliable and Interpretable Document Question Answering via VLMs
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911152523444224 |
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| author | Chen, Alessio Giovannini, Simone Gemelli, Andrea Coppini, Fabio Marinai, Simone |
| author_facet | Chen, Alessio Giovannini, Simone Gemelli, Andrea Coppini, Fabio Marinai, Simone |
| contents | Vision-Language Models (VLMs) have shown strong capabilities in document understanding, particularly in identifying and extracting textual information from complex documents. Despite this, accurately localizing answers within documents remains a major challenge, limiting both interpretability and real-world applicability. To address this, we introduce DocExplainerV0, a plug-and-play bounding-box prediction module that decouples answer generation from spatial localization. This design makes it applicable to existing VLMs, including proprietary systems where fine-tuning is not feasible. Through systematic evaluation, we provide quantitative insights into the gap between textual accuracy and spatial grounding, showing that correct answers often lack reliable localization. Our standardized framework highlights these shortcomings and establishes a benchmark for future research toward more interpretable and robust document information extraction VLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10129 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Reliable and Interpretable Document Question Answering via VLMs Chen, Alessio Giovannini, Simone Gemelli, Andrea Coppini, Fabio Marinai, Simone Computation and Language Information Retrieval Vision-Language Models (VLMs) have shown strong capabilities in document understanding, particularly in identifying and extracting textual information from complex documents. Despite this, accurately localizing answers within documents remains a major challenge, limiting both interpretability and real-world applicability. To address this, we introduce DocExplainerV0, a plug-and-play bounding-box prediction module that decouples answer generation from spatial localization. This design makes it applicable to existing VLMs, including proprietary systems where fine-tuning is not feasible. Through systematic evaluation, we provide quantitative insights into the gap between textual accuracy and spatial grounding, showing that correct answers often lack reliable localization. Our standardized framework highlights these shortcomings and establishes a benchmark for future research toward more interpretable and robust document information extraction VLMs. |
| title | Towards Reliable and Interpretable Document Question Answering via VLMs |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2509.10129 |