NLP Model Robustness Determination via Perturbation Attacks
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Solution Overview
Problem
Natural language processing (NLP) models, particularly text classification models, often fail to classify components with different tokens but similar semantics, leading to low robustness when analyzing data sets that differ from their training data.
Innovation Solution
The method involves performing white box and black box attacks on NLP models to identify modification rules that modify test data, determining potential trigger tokens, and generating modified test data to assess robustness, which can be used to improve the model's training and re-training processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NLP models are trained using a first data set, then the model can classify components of the first data set, but the model fails to classify components of a second data set with different tokens but similar semantics
Solution Approach 1:
The patent applies preliminary action by performing white box and black box attacks before the model is deployed to production. The system proactively identifies potential trigger tokens and generates modified test data that targets the model's robustness weaknesses in advance. This allows the model to be trained and re-trained with targeted data expansions before encountering these edge cases in production, preventing classification failures on semantically similar but token-different components.
2Reliability
If white box attacks are performed to identify modification rules, then robustness information can be determined, but the process requires access to model information such as loss function and embedding space
Solution Approach 1:
The patent uses an intermediary system that bridges the gap between simple black box testing and complex white box analysis. The system can operate in multiple modes: black box mode for basic robustness testing without model access, white box mode for comprehensive analysis when model information is available, and a hybrid mode that combines both approaches. This intermediary layer allows organizations to choose the appropriate attack methodology based on their specific needs and resource availability.
3Productivity
If NLP models are trained on training data, then the model achieves baseline performance, but the model lacks robustness when encountering data with different token compositions
Solution Approach 1:
The patent implements feedback by using the results of white box and black box attacks to guide the training and re-training process. The system identifies specific trigger tokens and modified test data that expose robustness weaknesses, then feeds this information back into the training data expansion process. This feedback loop ensures that the training data is strategically expanded to include edge cases and challenging examples, continuously improving the model's robustness while maintaining training efficiency through targeted rather than brute-force data generation.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for determining robustness information for an NLP model. Modification rules, such as replacement rules and/or insertion rules, are used to generate instances of modified test data based on instances of test data that comprise words and have a syntax and a semantic meaning. The instances of test data and modified test data are provided to the NLP model and the output of the NLP model is analyzed to determine output changing instances of modified test data, which are instances of modified test data yielded output from the NLP model that is different and/or not similar to the output yielded from the NLP model for the corresponding instance of test data. Robustness information for the NLP model is determined based at least in part on the output changing instances of modified test data.