Prompt Learning for Legacy ML Model Accuracy
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Solution Overview
Problem
Legacy machine learning models deployed in critical systems are difficult to replace or upgrade due to their mission-critical nature and interconnectedness, leading to performance bottlenecks as technology advances.
Innovation Solution
Implementing a prompt learning method that enhances deployed ML models by adding features using a second ML model, allowing for improved prediction accuracy without modifying the existing model, leveraging a pre-trained language model to enrich inputs and decouple enhancements from the original model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy ML models are deployed in mission-critical systems, then system reliability is maintained, but model accuracy becomes a bottleneck
Solution Approach 1:
The patent introduces prompt learning as an intermediary layer between input data and the legacy ML model. This mediator (prompt) enriches the input data with semantically similar features and contextual information, allowing the legacy model to achieve improved prediction accuracy without modifying its core structure or requiring system replacement.
Solution Approach 2:
The solution segments the prediction system into distinct components: the original legacy ML model remains unchanged while a separate prompt learning module processes input data independently. This segmentation allows the legacy model to continue operating reliably while the prompt module independently enhances input quality to improve accuracy.
2Measurement precision
If legacy ML models are replaced with newer models, then prediction accuracy is improved, but system stability and operational continuity are compromised
Solution Approach 1:
The prompt learning module performs preliminary action by pre-processing input data before it reaches the legacy ML model. It generates enriched prompts with semantically similar features and contextual information in advance, allowing the legacy model to make more accurate predictions without any changes to its structure or operational parameters.
Solution Approach 2:
The prompt serves as an intermediary that bridges the gap between modern data requirements and legacy model capabilities. It translates and enriches input data in a way that the legacy model can process effectively, achieving accuracy improvements without compromising system stability.
3Productivity
If legacy ML models are upgraded or replaced, then computing performance is improved, but system complexity and risk of failure increase
Solution Approach 1:
The prompt learning module acts as a lightweight intermediary that enhances computing performance without adding complex model architectures. It processes input data to generate enriched prompts, which are then fed to the existing legacy model, improving performance while maintaining simple system composition.
Solution Approach 2:
Instead of replacing the legacy model, the solution creates a copy or replica of its input processing capability through prompt learning. The prompt learning module learns to generate prompts that replicate the enhanced features needed by the legacy model, avoiding the complexity of model replacement while achieving performance improvements.
Data Source
AI summary
A method, computer system, and a computer program product for enhancing deployed machine learning (ML) models using prompt learning is provided. The present invention may include receiving an input for a prediction task using a first ML model. The present invention may also include adding a feature to the received input using a second ML model, where the added feature is recognized by the first ML model. The present invention may further include predicting, using the first ML model, an output for the received input based on the added feature, wherein the predicted output by the first ML model based on the added feature includes an improved accuracy relative to another predicted output by the first ML model without considering the added feature.