Cohort Determination for NLP Model Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models in natural language processing systems face performance issues when encountering inputs with characteristics not well-represented in their training data, leading to inaccurate recognition and interpretation, particularly for underserved cohorts.
Innovation Solution
A cohort determination component is introduced to identify and group similar inputs based on performance metrics, allowing for routing of inputs to models with better performance capabilities, using unsupervised or supervised machine learning algorithms to determine cohorts and improve model training by incorporating underserved data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine learning model is used for natural language processing, then device complexity is reduced, but performance accuracy deteriorates for underserved cohorts
Solution Approach 1:
The patent segments the natural language processing system into multiple machine learning models, each trained on specific cohorts or data subsets. This segmentation allows the system to handle different input types with specialized models, improving accuracy for underserved cohorts while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system dynamically selects which machine learning model to use based on cohort identification of the input data. This dynamic approach allows the system to adapt to different input characteristics in real-time, improving performance accuracy without requiring all models to be active simultaneously, thus managing complexity effectively.
2Measurement precision
If training data is limited to available datasets, then training time is reduced, but measurement precision deteriorates for underrepresented demographics
Solution Approach 1:
The patent applies preliminary action by pre-training multiple machine learning models on different cohorts before deployment. This allows the system to have pre-prepared specialized models for various demographics, improving recognition accuracy for underrepresented groups without requiring extensive training time during actual operation.
Solution Approach 2:
The system changes training parameters by training different models on different cohorts with specific characteristics. This parameter variation in training data composition improves measurement precision for diverse demographics while managing training time through targeted, cohort-specific training rather than exhaustive training on all possible data.
3Adaptability or versatility
If machine learning models are trained on diverse data, then adaptability improves, but loss of information increases due to insufficient representation of specific cohorts
Solution Approach 1:
The patent segments the training data into distinct cohorts and trains separate machine learning models for each cohort. This segmentation ensures that each model receives focused, high-quality training data for its specific cohort, preventing information loss that would occur if all data were mixed together in a single general-purpose model.
Solution Approach 2:
The system applies local quality by optimizing each machine learning model for its specific cohort's characteristics rather than using a uniform approach. This allows each model to have high-quality, cohort-specific representations, improving adaptability to different demographics while maintaining high information quality for each specific group.
Data Source
AI summary
Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.