Interpret, prune and distill Donut : towards lightweight VLMs for VQA on document
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915525176590336 |
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| author | Mansour, Adnan Ben Karine, Ayoub Naccache, David |
| author_facet | Mansour, Adnan Ben Karine, Ayoub Naccache, David |
| contents | Recent advances in Visually-rich Document Understanding rely on large Vision-Language Models like Donut, which perform document-level Visual Question Answering without Optical Character Recognition. Despite their effectiveness, these models are too costly for real-time or resource-constrained applications. We investigate model compression through knowledge distillation, training compact student models from a larger teacher. We leverage mechanistic interpretability to drive student architecture design within this framework. By analyzing internal computations, we identify essential subcomponents to retain, while having a clear view of which subcomponents should be approximated, skipped, or reparametrized based on their function. This approach yields Donut-MINT (Mechanistic Interpretability-based Network Trimming), a pruned Donut variant that reduces inference time and memory usage while maintaining strong performance on DocVQA, a standard benchmark for document Visual Question Answering. Our method reframes compression as circuit discovery, bridging interpretability research and practical Vision-Language Model deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpret, prune and distill Donut : towards lightweight VLMs for VQA on document Mansour, Adnan Ben Karine, Ayoub Naccache, David Computer Vision and Pattern Recognition Recent advances in Visually-rich Document Understanding rely on large Vision-Language Models like Donut, which perform document-level Visual Question Answering without Optical Character Recognition. Despite their effectiveness, these models are too costly for real-time or resource-constrained applications. We investigate model compression through knowledge distillation, training compact student models from a larger teacher. We leverage mechanistic interpretability to drive student architecture design within this framework. By analyzing internal computations, we identify essential subcomponents to retain, while having a clear view of which subcomponents should be approximated, skipped, or reparametrized based on their function. This approach yields Donut-MINT (Mechanistic Interpretability-based Network Trimming), a pruned Donut variant that reduces inference time and memory usage while maintaining strong performance on DocVQA, a standard benchmark for document Visual Question Answering. Our method reframes compression as circuit discovery, bridging interpretability research and practical Vision-Language Model deployment. |
| title | Interpret, prune and distill Donut : towards lightweight VLMs for VQA on document |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.26235 |