Machine Learning Training Sample Generation via Self-Service Loop
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Solution Overview
Problem
Generating training samples for machine learning mechanisms that translate natural language queries into structured queries is a time-consuming process.
Innovation Solution
A method for generating training samples involves receiving a structured query, finding semantically similar structured-query, natural-language-query training pairs, training a machine learning instance using these pairs, and validating the generated natural language queries to add them as training pairs to the set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to generate training samples for machine learning mechanisms, then training data can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The system uses the trained machine learning model itself to generate training samples by having it translate structured queries to natural language queries. The generated outputs are then validated and added back to the training set, creating a self-service loop that continuously expands training data without requiring manual creation of each sample.
Solution Approach 2:
The system performs preliminary validation of generated training pairs before adding them to the training set. By pre-validating the quality and accuracy of generated samples through automated checks, the system ensures that only high-quality training data is incorporated, maintaining training effectiveness while accelerating the overall process.
2Manufacturing precision
If more training samples are generated to improve model accuracy, then training quality increases, but the time and computational resources required increase
Solution Approach 1:
The system implements a feedback mechanism where generated training pairs are validated against the original structured queries and existing training data. This feedback loop ensures that only accurate and high-quality samples are added to the training set, maintaining training data quality while preventing the accumulation of erroneous or redundant samples that would waste computational resources.
Solution Approach 2:
The system dynamically adjusts the complexity and scope of validation checks based on the confidence level of the generated translations. For high-confidence translations, lighter validation is applied, while lower-confidence translations undergo more rigorous checking. This parameter adjustment optimizes the balance between validation thoroughness and processing efficiency.
Data Source
AI summary
Mechanisms for training a machine learning server instance are provided, the mechanisms including: receiving a structured query from a log; finding structured-query, natural-language-query training pairs that have a semantically similar structured query to the received structured query in a training set; training a machine learning instance using the structured-query, natural-language-query training pairs; providing the received structured query to the machine learning instance after being trained; in response to providing the received structured query to the machine learning instance, receiving a natural language query from the machine learning instance; validating the received natural language query; and adding the received structured query and the validated natural language query as a training pair to the training set.