Inference Model Tuning With Augmented Questions and Confidence Scoring
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Solution Overview
Problem
Existing inference models for managing data processing systems lack transparency and trustworthiness, making it difficult for downstream consumers to rely on their failure predictions without manual validation, which is time-consuming and inefficient.
Innovation Solution
Implementing explainable AI to extract hidden knowledge from inference models, generate augmented questions, and calculate confidence scores to improve the interpretability and trustworthiness of failure predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation of failure predictions is performed, then the reliability of predictions is improved, but the time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs self-validation by automatically generating explanations for its own predictions and assessing their credibility without requiring external manual validation. The inference model generates natural language explanations that justify its failure predictions, and the system automatically evaluates the quality of these explanations to determine prediction reliability.
Solution Approach 2:
The system implements a feedback mechanism where the quality of generated explanations is evaluated and used to adjust the credibility scoring of predictions. The system continuously learns from the evaluation results, refining its ability to assess prediction reliability and improving the overall validation process over time.
2Reliability
If explainable AI is implemented to extract hidden knowledge, then the interpretability of predictions is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary explanation generation module that translates the internal decision-making processes of the inference model into human-understandable natural language explanations. This intermediary layer makes the complex model operations interpretable without requiring fundamental changes to the underlying model architecture.
Solution Approach 2:
The system replaces manual interpretation and validation processes with automated natural language generation and evaluation mechanisms. Instead of requiring human experts to manually analyze model decisions, the system uses AI-driven explanation generation and automated credibility assessment to substitute the mechanical manual validation process.
3Reliability
If confidence scores are calculated for predictions, then the trustworthiness of predictions is improved, but the computational overhead increases
Solution Approach 1:
The system applies partial validation by focusing computational resources on evaluating only the most critical aspects of predictions rather than performing exhaustive analysis. The credibility assessment concentrates on key explanation quality metrics that have the greatest impact on prediction trustworthiness, avoiding unnecessary computational overhead from complete exhaustive validation.
Data Source
AI summary
Methods and systems inference model training and fine-tuning are disclosed. Augmented questions may be generated to augment an original question posed to an inference model to cause the inference model to explore and activate resources of the inference model that otherwise would not have been explores and activated by the original question. Prediction responses generated using these augmented questions to provide better insight into the generated predictions by including a confidence score for each generated prediction that is included in the prediction response.