Automated Parameter Tuning in Layered Model Frameworks
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Solution Overview
Problem
In layered model frameworks, it is challenging to isolate the performance of upstream models from downstream models, making it difficult to determine which version of an upstream model performs better, as the results from downstream models can be influenced by various factors such as content quality and impression rates.
Innovation Solution
The system automatically tunes parameters in a layered model framework by testing multiple versions of a first model and selecting parameter values for a downstream model that match specific metrics, allowing for confident evaluation of different upstream model versions without manual intervention, thereby reducing the time and effort required for parameter selection and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple models are chained together in a layered framework to improve content selection accuracy, then the overall system performance is improved, but it becomes difficult to isolate and evaluate the performance of individual upstream models
Solution Approach 1:
The patent segments the evaluation process by introducing intermediary metrics (impression rate, content quality score) that break down the downstream model's output into attributable components. This allows performance measurement at each layer independently while maintaining the layered framework structure.
Solution Approach 2:
The patent introduces intermediary metrics as mediators between upstream and downstream models. These metrics serve as intermediate evaluation points that capture the contribution of upstream models without requiring direct observation of final downstream output, thus enabling isolated performance assessment.
2Measurement precision
If manual testing and evaluation of different model versions is performed to determine performance, then accurate performance comparison can be achieved, but the time and effort required increases significantly
Solution Approach 1:
The patent implements self-service evaluation where the system automatically computes performance metrics and compares model versions without manual intervention. The intermediary metrics enable automatic attribution of performance differences to specific model changes, eliminating the need for manual experimentation while maintaining measurement precision.
Solution Approach 2:
The patent changes the evaluation parameters from direct downstream output comparison to intermediary metric comparison (impression rate, content quality). This parameter transformation enables automated, rapid evaluation while preserving the ability to accurately compare model versions through these more directly attributable metrics.
3Reliability
If parameters of downstream models are fixed to match metrics from upstream models, then performance isolation is achieved, but the complexity of parameter tuning increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the relationship between upstream output metrics and downstream input parameters. The system proactively configures downstream model parameters to match upstream model outputs before evaluation begins, establishing the isolation framework in advance rather than adjusting it during the evaluation process.
Data Source
AI summary
Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.