mlsanityData to 14 Oct 2026

FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding

arXiv 2505.17330 ↗First posted 22 May 2025cs.CV cs.AI cs.CLcited by 10

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Abstract (arXiv)

In this work, we propose Few Shot Domain Adapting Graph (FS-DAG), a scalable and efficient model architecture for visually rich document understanding (VRDU) in few-shot settings. FS-DAG leverages domain-specific and language/vision specific backbones within a modular framework to adapt to diverse document types with minimal data. The model is robust to practical challenges such as handling OCR errors, misspellings, and domain shifts, which are critical in real-world deployments. FS-DAG is highly performant with less than 90M parameters, making it well-suited for complex real-world applications for Information Extraction (IE) tasks where computational resources are limited. We demonstrate FS-DAG's capability through extensive experiments for information extraction task, showing significant improvements in convergence speed and performance compared to state-of-the-art methods. Additionally, this work highlights the ongoing progress in developing smaller, more efficient models that do not compromise on performance. Code : https://github.com/oracle-samples/fs-dag

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 38 of its 63 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

38 of 63 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 2.

10 papers.

  1. 2025Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systemscited by 14
  2. 2025Aligning LLMs for Multilingual Consistency in Enterprise Applicationscited by 11
  3. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  4. 2025SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Modelscited by 5
  5. 2025AccessEval: Benchmarking Disability Bias in Large Language Modelscited by 5
  6. 2026Comparative Study of Domain-adapted VLMs for General Document Visual Question Answeringcited by 3
  7. 2025FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Modelscited by 2
  8. 2025PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applicationscited by 1
  9. 2026Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesiscited by 1
  10. 2025Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Modelscited by 0