AI Model Signal Awareness via Minimal Token Subsequence
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Solution Overview
Problem
Existing measurement techniques for machine learning models do not adequately assess whether the models are capturing the right signals for their predictions, leading to potential inaccuracies in tasks like vulnerability detection in source code analysis.
Innovation Solution
A system and method for evaluating signal awareness of trained machine learning models by iteratively reducing input sequences while preserving predictions, using a token manager, validator, and signal manager to identify a minimal sub-sequence that maintains the model's prediction, and measuring signal awareness through a signal awareness recall metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing measurement techniques (e.g., F1 score) are used to evaluate model predictions, then prediction accuracy can be measured, but the ability to assess whether the model is capturing the right signals is lost
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: a token manager that identifies minimal subsequences, a validator that ensures prediction preservation, and an evaluator that computes signal awareness metrics. This segmentation allows each component to specialize in a specific aspect of the evaluation process, enabling precise signal measurement while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary evaluation framework that acts as a mediator between the model's raw predictions and the final signal awareness assessment. This intermediary layer processes predictions through minimal subsequence identification and validation, transforming basic accuracy metrics into comprehensive signal awareness measurements without requiring direct modification of the model architecture.
2Measurement precision
If minimal sub-sequence identification is performed to preserve predictions, then signal awareness can be accurately measured, but computational resources and time are increased
Solution Approach 1:
The system performs preliminary identification of minimal subsequences before conducting the full signal awareness evaluation. By pre-processing the input sequences to extract only the essential tokens that preserve predictions, the system reduces the computational burden of subsequent evaluation steps, achieving precise signal measurement with reduced time loss.
Solution Approach 2:
The patent extracts only the critical minimal subsequences from the full input sequences that are necessary to preserve model predictions. This extraction process removes redundant information while retaining the essential signal-carrying tokens, enabling accurate signal awareness measurement with reduced computational requirements and evaluation time.
3Adaptability or versatility
If traditional prediction performance metrics are used, then model accuracy is assessed, but fairness in evaluation of signal capture ability is compromised
Solution Approach 1:
The evaluation system applies local quality analysis by assessing signal awareness at the level of individual minimal subsequences rather than treating all input tokens uniformly. This localized evaluation approach allows the system to adapt to different signal patterns in various contexts, achieving fair and versatile evaluation while making signal capture measurement more detectable through granular analysis.
Data Source
AI summary
A system, computer program product, and method are provided for probing model signal awareness. An iterative process is employed to systematically isolate one or more relevant tokens of an input sequence to generate a reduced input sequence. The reduced input sequence is validated and presented to a trained artificial intelligence (AI) model and prediction output is generated. The reduction process is continued while the prediction output stays the same as that of the input sequence, and until a minimal sub-sequence is identified. A signal existence in the minimal sub-sequence is verified and signal awareness of the trained AI model is evaluated. The evaluation includes measuring the verified signal existence against an original signal from the input sentence.