FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models

2025-10-02

AI Summary

Enterprise-level document understanding models face critical challenges including scarcity of high-quality annotated data, stringent privacy and regulatory constraints, and prohibitively high manual annotation costs—often reaching millions of dollars. To address these, we propose a synthetic document generation framework grounded in stochastic schemas and parametric sampling, the first to jointly model layout patterns, visual structures, and multilingual content variability via a unified probabilistic formulation. This enables controllable, high-fidelity, large-scale synthesis of semi-structured documents. Unlike conventional template-based approaches, our method achieves up to an 11-percentage-point improvement in F1 score on key information extraction tasks while reducing annotation costs by over 90%. The framework has been successfully deployed in production systems, significantly accelerating model iteration cycles and real-world deployment.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such data is prohibitively expensive due to privacy constraints, legal restrictions, and the sheer volume of manual annotation needed - costs that can scale into millions of dollars. We introduce FlexDoc, a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. By probabilistically modeling layout patterns, visual structure, and content variability, FlexDoc enables the controlled generation of diverse document variants at scale. Experiments on Key Information Extraction (KIE) tasks demonstrate that FlexDoc-generated data improves the absolute F1 Score by up to 11% when used to augment real datasets, while reducing annotation effort by over 90% compared to traditional hard-template methods. The solution is in active deployment, where it has accelerated the development of enterprise-grade document understanding models while significantly reducing data acquisition and annotation costs.

Problem

• Generating diverse multilingual synthetic documents for training
• Reducing high annotation costs and privacy constraints in data collection
• Improving document understanding models with scalable synthetic data

Innovation

• Parameterized sampling generates diverse multilingual documents
• Stochastic schemas model layout patterns and visual structure
• Framework reduces annotation effort by over ninety percent