Model Upgrade Regression Gates
When it comes to deciding when a new frontier model is ready to replace the old one, implementing a set of regression gates becomes crucial. These gates serve as a framework to ensure the new model's performance meets specific requirements and can minimize potential risks associated with model upgrades. In this section, we will discuss the key aspects of using regression gates for effective governance of frontier models.
Freeze a Golden Test Set
Before starting the regression testing process, it is essential to freeze a golden test set. A golden test set represents a set of representative samples that accurately represent the data distribution of the target domain. This set will serve as the baseline for comparing the performance of the old and new models during the regression testing phase.
Set Pass Thresholds Before You Run
Once you have your golden test set ready, it's time to define the pass thresholds for each regression test. Pass thresholds indicate the minimum performance level the new model must achieve to pass the regression testing. Establishing these thresholds helps ensure that the new model meets the expected performance standards before proceeding to the next steps.
Test Safety and Refusal Behaviour Separately
To ensure that the new model meets the desired safety and refusal behaviour, it is crucial to test these aspects separately. Test safety refers to verifying that the model properly identifies and handles situations that should not be classified or predictions should not be made. Refusal behaviour checks whether the model accurately declines to make predictions when it should not.
Separating these tests allows you to focus on ensuring the new model behaves as expected in safety-critical scenarios, while also validating its capability to refuse predictions when necessary. This helps avoid false positives or negatives that could have severe implications in real-world applications.
Shadow-Run Against the Old Model
Before applying the new model, it is essential to conduct a shadow run against the old model. A shadow run involves running the new model alongside the old model on the same input data, ensuring that the predictions produced by both models are in alignment. This step helps ensure that the new model does not introduce any significant performance regression or introduce unexpected behaviour compared to the old model.
Staged Rollout with Rollback Triggers
After conducting the shadow run, the next step is to stage the rollout of the new model. During this phase, a portion of the data is used to gradually transition from the old model to the new model. This staged rollout allows you to monitor the performance and behaviour of the new model in a controlled environment.
To ensure the ability to revert to the old model in case of any issues, rollback triggers should be implemented. Rollback triggers ensure that if any regressions or unexpected behaviors are identified, you can quickly switch back to the old model without causing significant disruption or harm to the system.
Sign-Off and Record
Once the staged rollout with rollback triggers is complete and any potential issues have been addressed, it is time to sign off on the new model. Signing off indicates that the new model has met the performance and behavioural requirements and can be deployed as the primary model.
After obtaining sign-off, it is crucial to document the process, steps taken, and any identified issues or improvements. This documentation serves as a reference for future upgrades, enabling the team to learn from the experience and improve the governance process for future model upgrades.
Performance Monitoring
After deploying the new model, it is essential to monitor its performance over time. Continuous monitoring helps ensure that the new model maintains its expected performance and behavior. Monitoring can include metrics such as accuracy, precision, recall, F1 score, and other relevant metrics.
If the performance of the new model starts to deviate significantly from the expectations, it is essential to roll back to the previous version and investigate the issues. Documenting the reasons for rolling back and the steps taken can help identify potential causes of performance degradation and improve the governance process for future upgrades.
Monitoring and Tracking Metrics
In addition to performance monitoring, it is crucial to track metrics that reflect the new model's behavior. By tracking metrics such as false positive rate, false negative rate, and any other relevant metrics, you can ensure that the new model maintains the desired behavior and does not introduce any unintended biases or errors.
Documenting the metrics and any observed changes in the metrics will help identify potential issues and improve the governance process for future upgrades.
Tracking Model Performance and Behavior
To ensure that the new model performs as expected and maintains desired behavior, it is essential to track performance and behavior. Tracking metrics like accuracy, precision, recall, and other relevant metrics will help identify potential deviations from the desired performance and behavior. Documenting any observed changes in these metrics will aid in understanding the reasons behind performance degradation or unexpected behavior and improve the governance process for future upgrades.
Tracking User Interactions
In addition to tracking metrics, it is important to monitor user interactions with the new model. This involves recording and analyzing user feedback, including observations of unexpected outcomes, errors, or biases produced by the new model. Documenting these observations will help identify potential issues and improve the governance process for future upgrades.
Evaluating and Documenting Improvements
Once the new model has been deployed and tested, it is essential to evaluate and document any improvements observed during the rollout. This can include improvements in accuracy, precision, recall, and other relevant metrics, as well as any positive user feedback or reduced errors. Documenting these improvements will help identify the reasons behind the positive outcomes and improve the governance process for future upgrades.
Put it into practice
The Real-Time Model Verification Kit from FrontierLattice collects the templates and checklists behind this guide in ready-to-use form, so your team can apply the framework without drafting from scratch.