Heuristic Inter-Training for Few-Shot ML Model Fine-Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning networks face challenges in training with very few or incorrectly labeled data, particularly for tasks like conversation summarization, where existing methods require large amounts of labeled data and are time-consuming and inaccurate.
Innovation Solution
A system that splits unlabeled data into groups based on different perspectives, generates weakly labeled data using heuristics, inter-trains a pre-trained model for each perspective, and fine-tunes it using few-shot training data to produce a final model for multi-perspective summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of labeled data are used for training machine learning networks, then model performance is improved, but training time and resource consumption increase
Solution Approach 1:
The training process is segmented into multiple stages: pre-training on unlabeled data, inter-training with weakly labeled data from multiple perspectives, and fine-tuning with少量 labeled data. This segmentation allows the model to achieve high performance without requiring large amounts of labeled data for the entire training process
Solution Approach 2:
The model performs pre-training on unlabeled data before the main training task. This preliminary action allows the model to learn general patterns and representations from abundant unlabeled data, reducing the amount of labeled data needed for subsequent fine-tuning
2Manufacturing precision
If manual labeling of training data is performed to ensure accuracy, then data quality is improved, but time consumption and cost increase
Solution Approach 1:
The system uses automated heuristic methods to generate weakly labeled data from unlabeled data. The model itself participates in creating training labels through inter-training with multiple perspectives, eliminating the need for manual labeling while maintaining sufficient data quality for effective training
Solution Approach 2:
Weakly labeled data generated by heuristics serves as an intermediary between unlabeled data and fully labeled data. This intermediate labeling approach provides sufficient guidance for training without requiring the time and resources of manual labeling
3Productivity
If few-shot training data is used for fine-tuning, then training efficiency is improved, but model accuracy may deteriorate
Solution Approach 1:
The model undergoes pre-training on unlabeled data and inter-training with weakly labeled data before fine-tuning with few-shot labeled data. These preliminary actions prepare the model with robust representations, allowing it to achieve high accuracy even with limited labeled data for fine-tuning
Solution Approach 2:
The training process is divided into multiple phases with different data requirements. The few-shot fine-tuning is just the final phase, preceded by phases that use abundant unlabeled and weakly labeled data, ensuring the model accumulates sufficient knowledge before the accuracy-critical fine-tuning stage
4Reliability
If traditional training methods are used with labeled data, then model performance is maintained, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The training framework is designed to handle multiple data types (unlabeled, weakly labeled, and labeled data) within a unified architecture. The same model structure and training pipeline can process different data types at different stages, reducing overall system complexity despite the multi-stage approach
Data Source
AI summary
An example system includes a processor to receive unlabeled data, few-shot training data, and a pre-trained model. The processor can split the unlabeled data into a number of groups corresponding to different perspectives. The processor can generate weakly labeled data for each of the number of groups using a respective associated heuristic. The processor can inter-train a model for each different perspective based on respective weakly labeled data. The processor can fine-tune each inter-trained model based on the few-shot training data for each different perspective to generate a final model for each different perspective.