Zero-Shot Document Extraction via CDQA Model Selection
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Solution Overview
Problem
Traditional machine learning paradigms for information extraction are computationally costly, require extensive training data, are domain-limited, and suffer from compounded uncertainty when applied to unfamiliar documents, making them inefficient for generalizable document extraction across various domains.
Innovation Solution
The system employs a zero-shot learning approach using closed-domain question answering (CDQA) models to extract information from documents without the need for specific domain training, leveraging a knowledge graph and CDQA model repository to determine fields of interest and provide answers directly, thus bypassing the limitations of traditional machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning paradigms are used for information extraction, then extraction accuracy can be achieved in specific domains, but computational cost and training requirements increase significantly
Solution Approach 1:
The system uses pre-trained CDQA models that have been copied from specific domains and adapted through prompt engineering rather than retraining. The models are selectively applied to different document types based on their training domain, avoiding the computational cost of training new models while maintaining extraction accuracy.
Solution Approach 2:
The system creates a universal information extraction framework that can handle multiple document types and domains using a single set of CDQA models. By implementing domain-agnostic processing with prompt-based adaptation, the system achieves multi-functionality without requiring separate trained models for each domain, thereby reducing computational costs.
2Measurement precision
If traditional machine learning paradigms are used for information extraction, then domain-specific extraction accuracy can be achieved, but the system becomes domain-limited and cannot generalize to unfamiliar documents
Solution Approach 1:
The system introduces prompt engineering as an intermediary layer between the pre-trained CDQA models and the target documents. This intermediary mechanism allows the models to adapt to different domains without retraining by modifying the input prompts, thereby achieving both domain-specific accuracy and cross-domain generality.
Solution Approach 2:
The system changes the parameter of input prompts rather than changing the model weights or architecture. By adjusting prompt parameters (questions, context, formatting) based on document type and domain, the system achieves adaptation to different domains while maintaining the same underlying models, thus improving versatility without sacrificing accuracy.
3Reliability
If traditional machine learning paradigms are used for information extraction, then trained models can capture domain-specific verbiage, but extensive training data and time are required
Solution Approach 1:
The system performs preliminary action by pre-training CDQA models on diverse domains in advance. These pre-trained models are then stored and selectively applied to new documents based on their domain expertise, eliminating the need for time-consuming training when processing new documents while maintaining domain adaptation capability.
Solution Approach 2:
The system enables self-service by allowing pre-trained CDQA models to automatically identify and extract information from documents in their trained domains without requiring retraining. The models serve themselves by selecting appropriate documents based on domain matching, reducing both training time and manual intervention while maintaining reliability.
4Measurement precision
If traditional machine learning paradigms are used for information extraction, then models can be trained for specific tasks, but the system complexity and engineering skill requirements increase
Solution Approach 1:
The system extracts and separates the domain adaptation functionality from the model training process. By using prompt engineering to handle domain-specific adaptations rather than training models for each task, the system reduces complexity while maintaining task-specific accuracy. The complex training process is replaced with simpler prompt configuration.
Solution Approach 2:
The system uses disposable prompts instead of expensive, complex trained models for each specific task. Prompts can be quickly created, modified, and discarded based on document requirements without the need for lengthy training processes, thereby reducing system complexity and engineering requirements while maintaining extraction accuracy.
Data Source
AI summary
A system for performing document extraction is configured to: (a) receive a first document; (b) extract the first document into document elements, the document elements including pages, lines, paragraphs, or any combination thereof; (c) determine a first set of fields of interest for the first document, wherein the first set of fields of interest are determined via a type of the first document or via a first set of queries for probing the first document; (d) determine, from a plurality of closed domain question answering (CDQA) models, a first set of CDQA models that provides answers to each field of interest included in the first set of fields of interest; and (e) provide answers to the first set of fields of interest to the client device.