LLM-Based Training Dataset Feature Expansion for Accuracy and Diversity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning (ML) models are limited by the quality and diversity of their training datasets, which can affect prediction accuracy and versatility.
Innovation Solution
Utilize a large language model (LLM) to analyze and enhance training datasets by generating additional features and data subsets, improving the scope and comprehensiveness of the training data through feature type inference and data adjustment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning training datasets are used, then the ML models can be trained with existing data, but the prediction accuracy and versatility are limited due to data quality and diversity issues
Solution Approach 1:
A large language model is introduced as an intermediary component between the existing training dataset and the final enhanced dataset. The LLM analyzes data characteristics, generates synthetic data subsets, and transforms existing data into augmented versions, thereby resolving the contradiction between maintaining data quality and increasing data diversity for improved model versatility
Solution Approach 2:
The system changes the parameters of the training dataset by generating new data subsets with different characteristics, transformations, and augmentations. By modifying data parameters such as adding synthetic samples, transforming existing data, and creating diverse feature representations, the system simultaneously improves prediction accuracy while enhancing data diversity
2Adaptability or versatility
If the training dataset is expanded with more diverse data, then model versatility improves, but data quality and consistency may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the large language model continuously analyzes the characteristics of generated and augmented data subsets, evaluates their quality, and adjusts the data generation and augmentation processes accordingly. This feedback loop ensures that data diversity is increased while maintaining consistent quality standards through iterative refinement
Solution Approach 2:
By carefully controlling and adjusting data transformation parameters, the system maintains data quality while increasing diversity. The LLM monitors parameter changes during data augmentation and synthetic data generation, ensuring that quality thresholds are met while achieving the desired level of diversity for improved model versatility
3Manufacturing precision
If manual data analysis and feature engineering are performed, then data quality improves, but the processing time and complexity increase
Solution Approach 1:
The system replaces manual mechanical data analysis and feature engineering processes with an automated large language model. The LLM performs data characteristic analysis, subset generation, and feature engineering tasks automatically through intelligent algorithms, thereby maintaining high data quality while dramatically reducing processing time and eliminating manual labor
Solution Approach 2:
The large language model enables self-service data processing where the system automatically analyzes its own training data, identifies patterns, generates appropriate data subsets, and performs feature engineering without external intervention. This self-service capability maintains high data quality standards while reducing processing time by eliminating manual analysis steps
Data Source
AI summary
A method may include accessing a dataset including multiple data subsets, each of the data subsets corresponding to a feature of the dataset. Data in the data subsets may be analyzed to determine a characteristic of the data. In addition, a prompt template may be selected from prompt templates for the one of the data subsets based on the determined characteristic of the data. Prompts may be generated using the prompt template and the data from the one of the data subsets. The prompts may be provided to an LLM. The prompts may command the LLM to perform one or more operations with respect to the data of the one of the data subsets. One or more additional data subsets may be created for the dataset based on response of the LLM. Each of the one or more additional data subsets may correspond to a new feature of the dataset.