Synthetic Data Boosts Financial Document Parsing

Researchers have tackled the Privacy Paradox in Financial Document Understanding (FDU) by developing synthetic data generators to train models without using real client data. They created DocuLite, a framework with InvoicePy and TemplatePy, to generate complex synthetic OCR text and HTML-based invoice templates. These synthetic datasets were used to train models like OpenChat-3.5 and InternVL-2, resulting in significant improvements in F1 scores compared to models trained on conventional public datasets. This approach suggests that investing in synthetic data generation can be more effective for building document parsers in sensitive domains like finance and healthcare. This matters because it provides a privacy-compliant method to improve machine learning models for financial document processing.

Financial Document Understanding (FDU) presents a unique challenge due to the Privacy Paradox—balancing the need for complex, realistic data with the constraints of privacy laws. Traditional methods often rely on public datasets like UCSF or RVL-CDIP, which, while useful, tend to be too sanitized and simplistic to accurately reflect the intricacies of real-world financial documents. These documents often feature complex layouts, such as nested tables and colliding columns, which are not adequately represented in these datasets. This discrepancy can lead to models that perform well in controlled environments but falter in practical applications.

The innovative approach of using high-fidelity synthetic data offers a promising solution to this bottleneck. By employing a framework called DocuLite, which includes tools like InvoicePy and TemplatePy, researchers can generate synthetic OCR text and HTML-based invoice templates that mimic the complexity of real financial documents. This method ensures that models are trained on data that closely resembles the chaotic nature of actual invoices, without the risk of exposing sensitive personal information. The use of synthetic data allows for a controlled environment where the parameters of data generation can be finely tuned to enhance model training.

The results of this approach are significant. A 7B model, OpenChat-3.5, trained on synthetic data demonstrated a substantial improvement in performance, with a 0.525 increase in F1 score compared to models trained on traditional public datasets. Similarly, an 8B model, InternVL-2, showed a 0.513 improvement. These findings suggest that synthetic data can indeed outperform real, yet structurally simple, public data in training models for complex tabular extraction. This has profound implications for industries like finance and healthcare, where privacy concerns are paramount, and the accuracy of data extraction is critical.

For developers and data scientists working in sensitive domains, investing in synthetic data generation technologies could provide a better return on investment than relying solely on anonymized public datasets. By controlling the generation parameters, synthetic data can be tailored to better capture the structural nuances of real-world documents, leading to more robust and reliable models. This approach not only addresses privacy concerns but also enhances the model’s ability to generalize and perform effectively in diverse and complex environments. As the field of artificial intelligence continues to evolve, the strategic use of synthetic data could become a cornerstone of model training and development.

Read the original article here