GPT Model Evaluation Mechanism for Privacy-Compliant Testing
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Solution Overview
Problem
The non-deterministic nature of GPT-based generative models makes it challenging to compare outcomes between different model versions, system prompts, or configurations, which is essential for testing and deploying updates while adhering to data privacy regulations.
Innovation Solution
A novel testing mechanism that uses a GPT-based evaluation process to compare outputs from a new model version with those from an existing system in parallel, storing only metadata about the comparison to ensure compliance with data privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional regression testing methods are used to compare GPT model versions, then deterministic output comparison is required, but GPT models produce non-deterministic outputs making exact replication impossible
Solution Approach 1:
A GPT-based evaluation model serves as an intermediary to compare outputs from different GPT model versions. Instead of directly comparing deterministic outputs, the evaluation model analyzes and compares the non-deterministic outputs, providing a reliable measurement mechanism that bridges the gap between model versions while accommodating stochastic variability in GPT responses.
Solution Approach 2:
The patent creates a virtual copy of the evaluation process by using a GPT-based evaluation model that can replicate and compare outputs from different model versions. This copying approach allows multiple comparisons to be made without requiring actual re-execution of the full testing pipeline, enabling efficient and reliable version comparison while maintaining the non-deterministic nature of GPT outputs.
2Productivity
If historical customer data is stored for comparison between model versions, then regression testing can be performed, but data privacy regulations prohibit storage of customer data
Solution Approach 1:
The patent extracts only the necessary comparison information from customer interactions and feeds it to the GPT-based evaluation model. Instead of storing and reusing complete customer datasets, the system extracts minimal necessary data points for comparison and discards them after evaluation, maintaining testing efficiency while eliminating data privacy risks associated with historical data storage.
Solution Approach 2:
The evaluation process uses temporary, disposable data structures that hold minimal comparison information only during the evaluation process. These data objects are created for each comparison task and immediately discarded afterward, eliminating the need for persistent storage of customer data while maintaining the ability to perform efficient regression testing.
3Reliability
If A/B testing with real users is conducted to assess model performance, then practical performance can be measured, but the process is time-consuming and complex
Solution Approach 1:
The patent performs preliminary evaluation by using a GPT-based evaluation model to pre-assess model outputs before full A/B testing with real users. This preliminary action filters and ranks model versions based on automated evaluation criteria, reducing the number of candidates that require manual A/B testing and significantly cutting down the overall testing time while maintaining reliable performance measurement.
Data Source
AI summary
A method for improving the testability of any system using a GPT model to generate text or other content. The method operates by receiving user requests from the GPT model and a test GPT model and generating answers to the user requests from the GPT model and the test GPT model. After the answers are generated, comparing the generated answers using a GPT model to perform such comparing, then generating metadata from the comparing. Using the generated metadata, score the GPT model and the test GPT model. Then repeat the receiving, generation, comparing and scoring a number of times using different user requests. After repeating the number of times, determine whether the score of the test GPT model is higher than the score of the GPT model. If the test GPT model has a higher score than the GPT model, replace the GPT model with the test GPT model.