Surrogate Model for Interpretable NLP Classification
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Solution Overview
Problem
Modern natural language processing (NLP) systems, particularly those using deep learning, operate in a 'black box' fashion, making it difficult to interpret how they arrive at decisions, which is a concern for transparency and accountability in applications like financial institutions.
Innovation Solution
The proposed solution involves creating an interpretable surrogate model by scoring input data using an NLP model, generating a balanced sample of observations, and training a surrogate model to identify the most influential tokens. These tokens provide insight into the original NLP model's decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional contextual embedding is used to improve NLP model accuracy, then classification accuracy is improved, but model interpretability deteriorates
Solution Approach 1:
The patent creates a surrogate model that copies the predictive behavior of the complex NLP model but uses simple, interpretable features (bag-of-words) instead of high-dimensional contextual embeddings. This surrogate model replicates the classification accuracy while maintaining feature interpretability, allowing users to understand which words drive predictions without needing to interpret the black-box embedding space.
Solution Approach 2:
The surrogate model acts as an intermediary between the complex NLP model and the user. It translates the opaque high-dimensional embedding decisions into interpretable word-level explanations, serving as a mediator that preserves predictive accuracy while providing transparency about which tokens influenced the output.
2Measurement precision
If complex deep learning networks are used to enhance NLP capabilities, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential predictive information from the complex NLP model by training a surrogate model on bag-of-words features. This extraction process isolates the meaningful patterns without requiring the complexity of the original deep learning architecture, achieving accuracy while reducing model complexity.
Solution Approach 2:
The surrogate model serves as a simpler, more affordable alternative to the complex NLP model. It uses basic machine learning algorithms and simple features rather than expensive high-dimensional embeddings, providing a cost-effective way to achieve interpretability without sacrificing predictive performance.
3Productivity
If embedding transformation is applied to convert text to numerical vectors, then model prediction capability is improved, but ease of interpretation deteriorates
Solution Approach 1:
Instead of trying to interpret the high-dimensional embedding space directly, the patent inverts the approach by training a surrogate model that uses simple, human-readable word features. This inversion allows the system to maintain sophisticated prediction capability through the NLP model while providing easy interpretation through the surrogate model's simple feature space.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for distillation of a natural language processing model. An example method includes receiving, by communications circuitry, a set of text data comprising a set of observations and predicting, by processing circuitry and using the NLP model, classifications for each observation in the text data. The example method further includes generating, by model training engine, a balanced sampled data structure based on the predicted classifications for each observation in the text data and training, by the model training engine, a surrogate model using the balanced sampled data structure. The example method further includes identifying, by an interpreter and from the surrogate model, a set of most-influential tokens in the text data.