LLM-Based Multilingual Document Extraction Training from Synthetic Data
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Solution Overview
Problem
Existing machine learning models struggle with multilingual document information extraction, requiring excessive customer effort or prohibitively expensive specialized models to achieve accurate results.
Innovation Solution
A method utilizing a single master document and a Large Language Model (LLM) to generate translated ground truth documents, which are corrected by users, then used to create a training dataset for a first ML model, enabling it to accurately process documents in multiple languages without extensive user input or high costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a custom training option is offered to customers to address multilingual issues, then language support capability is improved, but customer effort and time required increase significantly
Solution Approach 1:
The system uses a Large Language Model to generate synthetic translated documents that copy the structure and content of original documents in the target language. These synthetic documents serve as training data, eliminating the need for customers to provide actual multilingual documents for training.
Solution Approach 2:
The Large Language Model acts as an intermediary between the customer's original documents and the training data generation process. It translates and generates synthetic documents that can be used for training without requiring direct customer involvement in the training process itself.
2Measurement precision
If custom training is performed for each language separately, then language-specific accuracy is improved, but resource consumption and complexity increase
Solution Approach 1:
The system uses a single multilingual Large Language Model that can handle multiple languages simultaneously. This universal model generates training data for different languages from a single base document, eliminating the need for separate training processes for each language while maintaining language-specific accuracy.
3Reliability
If premium specialized machine learning models are used to handle private data, then data security and compliance are improved, but service cost increases prohibitively
Solution Approach 1:
The system generates synthetic training documents that can be discarded after use, rather than requiring expensive specialized models. These synthetic documents are created on-demand from the customer's data and used solely for training the information extraction model, providing security without the high cost of premium models.
4Manufacturing precision
If customers submit documents with varying templates and layouts for training, then training data quality is improved, but data preparation time and effort increase
Solution Approach 1:
The Large Language Model performs self-service by automatically generating synthetic training documents from the customer's original documents. It handles the entire data preparation process including translation, synthesis, and formatting, eliminating the need for customers to manually prepare training data while maintaining high data quality.
Data Source
AI summary
A document information extraction service can utilize an LLM to provide support for extracting information from documents in multiple languages. A trained first machine learning model can map data extracted from a first master document of a first document type in a first language. The mappings can be corrected via user input to obtain ground truth data for the master document. The ground truth data can be translated into a second language and optionally corrected to obtain translated ground truth data. An LLM can generate a training dataset of fake documents of the first document type that contain text in the second language based at least in part on the translated ground truth data. The trained first machine learning model can be trained further with the training dataset and deployed to extract data from documents of the first document type that contain text in the second language.