Synthetic Training Data Generation for AI Model Adaptation
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Solution Overview
Problem
The training of conversational computing systems is time-consuming and challenging due to the need for thousands of human-annotated training examples, which can be inconsistent and require significant resources, and the evolution of data-flow programming languages or APIs necessitates updates in training data to maintain accuracy and adapt to changing user expectations.
Innovation Solution
A computer program that identifies instances of a find expression within a subset of previously-annotated training examples using a filtering constraint, replaces them with a replacement expression, and outputs an updated subset of training examples, allowing for targeted updates and reducing the computational time required for migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thousands of human-annotated training examples are used to train conversational computing systems, then the training accuracy and reliability are improved, but the training time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating synthetic training examples before actual training occurs. These synthetic examples are created using templates and rules that mimic human annotations, allowing the model to be pre-trained with large volumes of data without requiring extensive human annotation time during the actual training process.
Solution Approach 2:
The invention creates copies of existing training examples through synthetic data generation. By using template-based approaches and rule-based transformations, the system generates numerous variations of training examples that replicate the structure and quality of human-annotated data, thereby expanding the training dataset without proportional increases in human annotation effort.
2Manufacturing precision
If highly skilled human annotators are used to label training data with defined format and syntax, then the annotation quality and consistency are improved, but the cost and complexity of the training process increase
Solution Approach 1:
The system performs self-service by automatically generating and annotating training examples without requiring human annotators. The synthetic data generation process uses predefined templates, rules, and algorithms to create consistently formatted training data, thereby eliminating the need for skilled human annotators while maintaining annotation quality through automated quality control mechanisms.
Solution Approach 2:
The invention changes parameters by transforming the approach from human-dependent annotation to algorithm-driven generation. By adjusting the parameters of template systems and rule-based generators, the system can control the format, syntax, and quality characteristics of generated training examples, thereby maintaining precision while reducing human involvement and associated complexity.
3Adaptability or versatility
If training data is updated to adapt to evolving data-flow programming languages or APIs, then the system adaptability and relevance are improved, but the time and resources required for data migration increase
Solution Approach 1:
The system performs preliminary adaptation by generating synthetic training examples that are already aligned with current programming languages and APIs. When updates are needed, the synthetic data generation process can be reconfigured to produce examples using the new syntax and structures, allowing the training data to be updated without extensive manual migration of existing examples.
Solution Approach 2:
The invention introduces dynamics by making the training data generation process adaptable and flexible. The template-based system can dynamically adjust to changes in programming languages and APIs by modifying generation parameters and rules, thereby maintaining system relevance without requiring static, hard-to-update training datasets.
4Quantity of substance
If extensive human annotation resources are allocated to create training examples, then the quantity and diversity of training data are improved, but the cost and resource consumption increase significantly
Solution Approach 1:
The system creates numerous copies of training examples through automated synthetic data generation. By using templates and rule-based transformations, a single source example can generate multiple variations, thereby multiplying the effective quantity of training data without proportional increases in human annotation resources or energy consumption.
Solution Approach 2:
The synthetic data generation system performs self-service by automatically creating training examples without requiring human annotators. This automated process significantly reduces resource consumption compared to manual annotation while maintaining the ability to generate large quantities of diverse training data through algorithmic generation.
Data Source
AI summary
Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain an updated subset of training examples. The updated subset of training examples is output by the computer program, which may be used for training a machine learning model.