Data Shift-Resilient Unit Testing for Large Models

Solution Overview

Problem

Training and testing very large machine learning models (VLMs) are costly and time-consuming, making it challenging to ensure that changes to the model pipeline, dataset, or codebase do not adversely impact the model's behavior.

Innovation Solution

The use of compressed or proxy versions of VLMs for unit testing, combined with a framework that accounts for data distribution shifts, allows for cost-effective retraining and retesting of models while ensuring that changes do not significantly impact model behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If full-scale VLM training and testing is performed to ensure model validity after changes, then model behavior accuracy is maintained, but economic and time costs increase significantly

Engineering Contradiction:

Improvemodel behavior accuracyVSAvoidtraining and testing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent creates a compressed proxy model that replicates the behavior of the full-scale VLM for testing purposes. This copy allows validation of model changes without requiring expensive and time-consuming full-model retraining and testing, thus maintaining reliability while reducing resource consumption.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The compressed proxy model serves as a disposable testing artifact that can be quickly created and discarded. Instead of repeatedly training and testing expensive full-scale VLMs, the patent uses inexpensive compressed models for validation, significantly reducing economic and time costs while ensuring model behavior accuracy through targeted testing.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

2Reliability

If full-scale VLM training and testing is performed to ensure model validity after changes, then model behavior accuracy is maintained, but economic costs increase significantly

Engineering Contradiction:

Improvemodel behavior accuracyVSAvoidcomputational cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent creates a compressed proxy model that replicates the behavior of the full-scale VLM for testing purposes. This copy allows validation of model changes without requiring expensive and time-consuming full-model retraining and testing, thus maintaining reliability while reducing resource consumption.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The compressed proxy model serves as a disposable testing artifact that can be quickly created and discarded. Instead of repeatedly training and testing expensive full-scale VLMs, the patent uses inexpensive compressed models for validation, significantly reducing economic and time costs while ensuring model behavior accuracy through targeted testing.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Productivity

If unit testing is performed without accounting for data distribution shifts, then testing speed is maintained, but false positives and false negatives increase

Engineering Contradiction:

Improvetesting speedVSAvoidtest result accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary analysis of data distribution shifts before conducting unit tests on the compressed proxy model. By detecting distribution changes in advance, the system can adjust testing parameters or trigger additional validation steps, preventing false positives and negatives while maintaining testing efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms that monitor data distribution during testing and adjust the testing process accordingly. When distribution shifts are detected, the system provides feedback to modify test execution or interpretation, ensuring accurate results without sacrificing testing speed through intelligent adaptation rather than brute-force retesting.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250131323A1Data shift-resilient unit testing of very large models

Publication Date: 2025.04.24 DELL PROD LP

AI summary

One example method includes generating a first test metric using an unknown dataset and second test metrics using shifted datasets that are shifted versions of a known dataset. A data distribution difference is determined between the unknown dataset and one of the shifted datasets that is closest to the unknown dataset. A determination is made if the data distribution difference is less than or equal to a first known threshold, and applying the data distribution difference to a correlation model to determine an estimated test metric difference. A test metric difference id determined between the first test metric and a second test metric associated with the one of the shifted datasets that is closest to the unknown dataset. A determination is made if a difference between the test metric difference and the estimated test metric difference is less than or equal to a second known threshold.