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document understanding

Document understanding involves document classification, layout analysis, information extraction, and DocQA.

Papers

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Showing 101–125 of 309 papers

TitleStatusHype
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language ModelsCode0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
Assessing Generative AI value in a public sector context: evidence from a field experiment—0
DocMIA: Document-Level Membership Inference Attacks against DocVQA ModelsCode0
HERITAGE: An End-to-End Web Platform for Processing Korean Historical Documents in HanjaCode0
BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations—0
Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends—0
Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark—0
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models—0
Memory-Augmented Agent Training for Business Document Understanding—0
Learned Compression for Compressed LearningCode0
DocVLM: Make Your VLM an Efficient Reader—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time ScalingCode0
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks—0
MATATA: Weakly Supervised End-to-End MAthematical Tool-Augmented Reasoning for Tabular Applications—0
DOGE: Towards Versatile Visual Document Grounding and Referring—0
StructFormer: Document Structure-based Masked Attention and its Impact on Language Model Pre-Training—0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation—0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework—0
Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding—0
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding—0
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection—0
LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding—0
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