Automatic Prompt Update for Data Drift Correction
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Solution Overview
Problem
Existing machine learning models struggle to identify and dynamically correct data drift, leading to incorrect predictions and negative business impacts.
Innovation Solution
The implementation of an automatic prompt generation model that adapts and updates language model prompts in response to context changes, using a context management structure to store and retrieve previous contexts and update prompts accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the model is retrained to correct data drift, then the model accuracy is improved, but the computational cost and time required increase significantly
Solution Approach 1:
The patent extracts only the necessary prompt updates from the full retraining process. Instead of retraining the entire language model, it identifies and updates only the prompt portions that need correction based on drift detection, thereby maintaining model accuracy while significantly reducing computational cost.
Solution Approach 2:
The patent creates updated prompts as copies of the original prompts with modified portions. These copied prompts are then used for inference instead of retraining the model, allowing the system to adapt to data drift using minimal computational resources while maintaining high accuracy.
2Reliability
If the model is retrained to correct data drift, then the model accuracy is improved, but the time required for updates increases
Solution Approach 1:
The patent extracts only the necessary prompt updates from the full retraining process. Instead of retraining the entire language model, it identifies and updates only the prompt portions that need correction based on drift detection, thereby maintaining model accuracy while significantly reducing computational cost.
Solution Approach 2:
The patent performs preliminary drift detection and prompt generation before the actual model update is needed. By pre-processing and preparing updated prompts in advance, the system can quickly deploy corrections without time-consuming retraining operations when data drift occurs.
3Device complexity
If manual prompt updates are used, then the complexity of the system is reduced, but the productivity and responsiveness to data drift decrease
Solution Approach 1:
The patent implements a self-service system where the language model automatically detects data drift and generates updated prompts without human intervention. The system monitors its own performance, identifies drift conditions, and autonomously updates prompts to maintain accuracy, thereby increasing productivity while keeping the core architecture relatively simple.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors model performance and compares it against expected behavior. When drift is detected through performance degradation or drift detection algorithms, the system automatically triggers prompt updates based on the feedback loop, enabling rapid response to data drift without manual intervention.
Data Source
AI summary
Techniques for correcting data drift of a language model are disclosed. A model is built, and this model is designed to solve a same task for which the language model has been trained. The model is applied to new input data. This application results in generation of a prediction comprising predicted label data. Context is stored in a context management structure (CMS). The context includes a prompt template, a prediction, and labeled input data used to train the language model. The data drift is determined to have occurred. This determination is performed by determining that the context is within a threshold level of similarity to a previously stored context. In response to determining that the data drift has occurred, an operation is performed to correct the data drift.