4 papers
Cross-Lingual SynthDocs: A Large-Scale Synthetic Corpus for Any to Arabic OCR and Document Understanding
Haneen Al-Homoud, Asma Ibrahim, Murtadha Al-Jubran +5
Cross-Lingual SynthDocs is a large-scale synthetic corpus designed to address the scarcity of Arabic resources for Optical Character Recognition (OCR) and Document Understanding (D…
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
Kesen Wang, Daulet Toibazar, Pedro J. Moreno
We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question gen…
Trust the Model: Compact VLMs as In-Context Judges for Image-Text Data Quality
Daulet Toibazar, Kesen Wang, Sherif Mohamed +3
Vision-language models (VLMs) extend the conventional large language models by integrating visual data, enabling richer multimodal reasoning and significantly broadens the practica…
Multi-Agent Interactive Question Generation Framework for Long Document Understanding
Kesen Wang, Daulet Toibazar, Abdulrahman Alfulayt +6
Document Understanding (DU) in long-contextual scenarios with complex layouts remains a significant challenge in vision-language research. Although Large Vision-Language Models (LV…