Automated Domain-Specific Lexicon Generation for ML Training Data
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Solution Overview
Problem
Current machine learning (ML) model training datasets are often laborious, error-prone, and expensive to create manually, and training data from public sources like Wikipedia can contain errors, leading to less accurate language models.
Innovation Solution
The solution involves using a cloud provider network to automate the creation of domain-specific datasets through API calls, generating a domain-specific lexicon, and gathering data from focused sources to produce high-quality training data for ML models, which is then used to retrain language models for more accurate inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is manually created, then data quality can be controlled, but labor cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically collecting, cleaning, and preprocessing training data from multiple public sources before the actual model training process. Data extraction, normalization, and quality filtering are executed in advance through automated pipelines, eliminating the need for manual data preparation and reducing both time consumption and labor costs while maintaining data quality standards.
Solution Approach 2:
The system implements self-service by enabling automated data collection and processing capabilities that operate without human intervention. The automated pipelines continuously gather data from public sources, perform quality control checks, and prepare training datasets autonomously, allowing the ML system to serve itself with high-quality training data without requiring manual labor for data curation.
2Quantity of substance
If training data is gathered from public sources, then data availability increases, but errors and inaccuracies in the data increase
Solution Approach 1:
The system introduces an intermediary layer of automated data processing and quality control mechanisms between the public data sources and the ML training process. This intermediary layer includes automated error detection, data validation, and cleaning algorithms that filter out inaccurate information while preserving the benefits of large-scale data collection from multiple public sources.
Solution Approach 2:
The system implements feedback loops that continuously monitor data quality metrics from collected training data. Automated quality control processes analyze the gathered data, identify errors and inaccuracies, and adjust the data collection and preprocessing parameters accordingly. This feedback mechanism ensures that data availability is maintained while systematically reducing errors through iterative quality improvement.
3Measurement precision
If domain-specific data is used to retrain language models, then inference accuracy improves, but training complexity and computational resources increase
Solution Approach 1:
The system applies segmentation by dividing the domain-specific data into structured categories and organizing training data into specialized datasets for different domains. This segmentation approach allows the language model to be trained on targeted, relevant data subsets rather than attempting to process all domain-specific data uniformly, reducing training complexity while maintaining inference accuracy across multiple domains.
Data Source
AI summary
Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.