AI Output Confidence Sampling for Search Analytics Auditing
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is labor-intensive and time-consuming, and there is a lack of certainty in predicting outputs from language models, making accurate analysis of Generative AI outputs crucial but challenging.
Innovation Solution
An apparatus and method for auditing AI systems by repeatedly testing with similar inputs to ensure a sufficient sample size of non-deterministic outputs, using techniques like Dirichlet Process, Gibbs Sampling, and Generative Adversarial Networks to determine when a representative sample is achieved, thereby reducing resource wastage and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models use large amounts of labeled data for training, then model performance is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system uses the AI model itself to generate synthetic training data through self-testing with carefully crafted inputs. The model produces its own training examples by generating responses to prompt templates, eliminating the need for external human annotators to create labeled datasets.
Solution Approach 2:
The system pre-generates synthetic training data before actual model training by creating prompt templates and generating sample inputs. This preliminary data generation step prepares training materials in advance, reducing the time needed during the actual training process.
2Reliability
If deep learning models use large amounts of labeled data for training, then model performance is improved, but labor cost increases
Solution Approach 1:
The system uses the AI model itself to generate synthetic training data through self-testing with carefully crafted inputs. The model produces its own training examples by generating responses to prompt templates, eliminating the need for external human annotators to create labeled datasets.
Solution Approach 2:
The system creates copies of training data by generating synthetic examples that mimic real-world patterns. Instead of manually creating unique labeled datasets, the system generates multiple synthetic variations that capture the essential characteristics needed for training.
3Measurement precision
If the AI system is tested repeatedly to ensure sufficient sample size, then analysis accuracy is improved, but energy consumption and computational cost increase
Solution Approach 1:
The system performs a limited number of targeted tests with carefully designed prompt templates rather than exhaustive testing. By focusing on specific input patterns and using statistical sampling, the system achieves sufficient analysis accuracy without requiring excessive computational resources.
Solution Approach 2:
The system varies parameters such as prompt templates, input variations, and sampling sizes to optimize the balance between testing accuracy and computational cost. By adjusting these parameters, the system finds the minimum sufficient sample size needed for reliable analysis.
4Reliability
If the AI system is tested repeatedly to ensure sufficient sample size, then reliability of AI analytics is improved, but time consumption increases
Solution Approach 1:
The system performs a limited number of targeted tests with carefully designed prompt templates rather than exhaustive testing. By focusing on specific input patterns and using statistical sampling, the system achieves sufficient analysis accuracy without requiring excessive computational resources.
Solution Approach 2:
The system varies parameters such as prompt templates, input variations, and sampling sizes to optimize the balance between testing accuracy and computational cost. By adjusting these parameters, the system finds the minimum sufficient sample size needed for reliable analysis.
Data Source
AI summary
The technology disclosed relates to a method for inducing an agent-under-test (AUT) to disclose respective outputs in response to processing a target input probe. An agent sampling logic is configured to analyze respective outputs to determine whether the outputs represent a sample for the target input probe and to also determine a confidence level of the respective outputs being the sample for the target input probe.