Prompt Encoder TextGAN for ML Data Adaptation
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Solution Overview
Problem
Machine learning systems face challenges in maintaining expertise over time, leading to 'black box' systems where human knowledge is lost, making it difficult to process new data sets and retrain models effectively without extensive feature engineering.
Innovation Solution
The use of a prompt encoder and text generative adversarial network (textGAN) to transform new data sets into fused data sets that preserve original meaning while adopting the style of existing data sets, allowing for direct updating of machine learning systems without requiring human expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature engineering is performed by human subject matter experts to incorporate expert knowledge into machine learning systems, then the system can maintain expertise and interpretability, but it requires extensive human resources and experience, making it difficult to maintain and update
Solution Approach 1:
The system uses automated prompt encoding and text generative adversarial networks to perform feature transformation without human expert intervention. The model independently learns to generate prompts that encode new data in the style of existing data, eliminating the need for human subject matter experts to perform feature engineering while maintaining system expertise.
Solution Approach 2:
The patent replaces the manual mechanical process of human expert feature engineering with an automated computational system. The textGAN model automatically learns data distribution patterns and generates transformed data, substituting human cognitive processes with machine learning algorithms that can scale without additional human resources.
2Adaptability or versatility
If new data sets are added to existing data sets and feature engineering is re-done based on new expert knowledge, then the system can adapt to new data, but it requires extensive human resources and time, reducing productivity
Solution Approach 1:
The system performs preliminary learning of data distribution patterns from existing data before new data arrives. When new data is introduced, the pre-trained textGAN model can quickly adapt and generate transformed data without requiring complete re-engineering, enabling rapid adaptation while maintaining high productivity.
Solution Approach 2:
The patent changes the parameter representation of new data by learning data distribution characteristics and transforming new data to match the style and distribution of existing data. This parameter transformation approach allows the system to adapt to new data sets efficiently without manual feature engineering, significantly improving update productivity.
3Reliability
If the model is retrained based on new expert knowledge, then the system can incorporate updated expertise, but the effectiveness of the model cannot be guaranteed and extensive human resources are required
Solution Approach 1:
Instead of completely retraining the model with new expert knowledge, the system copies and adapts the data distribution patterns from existing successful models. The textGAN learns to generate data that mimics the style and characteristics of existing data, allowing the model to incorporate new information while preserving the effectiveness of the original trained model, thus reducing retraining time and uncertainty.
4Measurement precision
If extensive feature engineering is performed to ensure accurate prediction results, then the system achieves high accuracy, but it increases device complexity and reduces ease of operation
Solution Approach 1:
The system performs feature transformation automatically through prompt encoding and textGAN without requiring human experts to manually engineer features. This self-service approach maintains prediction accuracy by learning optimal data transformations while significantly improving ease of operation, as the system can be updated without human intervention.
Data Source
AI summary
Prompt learning is performed, using a prompt encoder, on an input data set to generate a revised text pattern. The revised text pattern is processed, using a text generative adversarial network, based on an existing data set to generate a fused data set and a machine learning system is updated with the fused data set.