LLM Knowledge Distillation for Robust Document Understanding
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Solution Overview
Problem
Existing AI systems, such as ML models in enterprise systems, face inaccuracies due to limited training data, leading to inefficiencies and errors in unforeseen scenarios, particularly in tasks like expense management, resulting in longer processing times and resource inefficiencies.
Innovation Solution
A machine learning model training system that leverages a large language model for knowledge distillation and multi-task learning, incorporating self-supervised alignment and retrieval-augmented generation to enhance training data, using OCR and classification models to improve model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI systems use limited training data, then model training is faster and requires fewer resources, but model accuracy and reliability deteriorate leading to errors in unforeseen scenarios
Solution Approach 1:
The system performs preliminary actions by using OCR and classification models to preprocess and prepare training data before the main ML training process. This includes extracting text from documents, classifying documents into categories, and generating structured training samples in advance, which improves model accuracy without requiring excessive training data volume during the main training phase
Solution Approach 2:
The patent introduces intermediary components including OCR models that convert document images to text, classification models that categorize documents, and summary generation systems that create structured training data. These intermediaries transform unstructured data into formatted training samples, enabling better model reliability with manageable data volumes
2Adaptability or versatility
If existing AI systems process diverse document types with basic models, then system complexity is reduced, but processing accuracy and adaptability worsen leading to inefficiencies in unforeseen scenarios
Solution Approach 1:
The system segments the document processing task into distinct functional modules: OCR module for text extraction, classification module for document categorization, summary generation module for creating training data, and ML model module for final processing. Each module handles a specific aspect of document processing, enabling high adaptability across diverse document types while keeping individual module complexity manageable
Solution Approach 2:
The patent creates a universal processing framework where the same modular architecture handles multiple document types (invoices, receipts, contracts, etc.) through configurable classifiers and processors. The system uses unified data structures and processing pipelines that can accommodate various document formats, achieving versatility without proportionally increasing complexity
3Measurement precision
If existing AI systems use simple processing pipelines, then processing speed is faster, but accuracy in complex scenarios worsens leading to longer processing times for corrections
Solution Approach 1:
The system performs accuracy-critical operations in advance: OCR text extraction and document classification are completed before main ML processing. This preliminary preparation ensures high accuracy in the subsequent training and inference phases without adding significant processing time during critical operations, maintaining productivity
Data Source
AI summary
Methods, systems, and computer-readable storage media directed to a machine learning (ML) model training system for training ML models by leveraging a large language model (LLM) for knowledge distillation to provide training data and using multi-task learning to train ML models using the training data.