Persona-Based AI Evaluation for Representative Application Testing
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Solution Overview
Problem
Existing methods for evaluating generative machine learning models are inadequate as they often fail to detect errors due to non-representative user queries and require user feedback, leading to potential trust issues and undetected performance issues.
Innovation Solution
Utilizing multiple generative machine learning models with varied personas to generate evaluation questions and compare responses to evaluate the performance of target models, employing techniques like n-grams and semantic similarity to assess accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual testing procedures are used with a small team of testers, then the evaluation process is simple and controllable, but the queries created are rarely representative of the diverse user base, leading to undetected errors
Solution Approach 1:
The patent creates virtual user personas that copy and simulate the diverse characteristics of real users (different backgrounds, writing proficiencies, and writing styles). These personas generate evaluation queries that accurately represent the diverse user base without requiring actual human users to participate in testing.
Solution Approach 2:
The evaluation system uses generative AI models to automatically generate evaluation queries and assess model performance autonomously. The system serves itself by using AI agents to create diverse test cases and evaluate responses, eliminating the need for manual intervention while maintaining comprehensive coverage of user scenarios.
2Reliability
If user feedback is used to detect errors, then real user experiences can be captured, but users who encounter errors lose trust in the application and feedback requires users to first encounter the errors themselves
Solution Approach 1:
The patent performs evaluation actions before actual user interaction by pre-generating diverse evaluation queries using virtual personas. The system proactively identifies potential errors and assesses model performance in advance, allowing issues to be addressed before users encounter them, thereby preventing trust loss.
Solution Approach 2:
The patent introduces virtual user personas as intermediaries between the model and real users. These personas simulate diverse user behaviors and generate evaluation queries that capture real user experiences without actual users needing to interact with the model, thus detecting errors without exposing real users to potential failures.
3Reliability
If multiple generative machine learning models with varied personas are used to generate evaluation questions, then comprehensive and representative evaluation coverage is achieved, but the system complexity increases
Solution Approach 1:
The patent employs a single generative AI model that performs multiple functions: generating diverse evaluation queries, simulating different user personas, and assessing model responses. This multi-functional approach achieves comprehensive evaluation coverage without requiring multiple separate models, thereby reducing overall system complexity.
Data Source
AI summary
Aspects of the present disclosure relate to evaluating performance of a generative machine learning model. Embodiments include using a plurality of generative machine learning models to generate evaluation questions, wherein each of the generative machine learning models is configured to use a given persona for generating one or more of the evaluation questions. Embodiments further include providing the evaluation questions as input to a target application. Embodiments further include generating an indication of a level of performance of the target application based on evaluating an answer generated in response to a question of the evaluation questions.