Generative Model Reinforcement Learning for Synthetic Training Data
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Solution Overview
Problem
The high cost of annotating data and data scarcity pose challenges in training and deploying effective event detection systems, particularly in domains like scientific papers where labeled training data is insufficient.
Innovation Solution
A generative model is trained using reinforcement learning techniques to generate labeled training data, which is then used to improve the performance of an event detection model by iteratively updating its parameters based on performance metrics, thereby addressing data scarcity and eliminating the need for noise filtering during the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-annotated training data is used to train event detection models, then model performance can be improved, but the cost of data annotation increases and data scarcity persists
Solution Approach 1:
The patent uses a generative model to create synthetic copies of labeled training data that mimic the characteristics and distribution of human-annotated data. These synthetic data copies serve as substitutes for expensive human-labeled data, enabling sufficient training data quantity without proportionally increasing annotation costs
Solution Approach 2:
The system employs reinforcement learning where the event detection model evaluates its own performance on synthetic data and generates feedback signals that automatically guide the generative model to produce improved synthetic training data. This self-service mechanism eliminates the need for external human annotators while continuously improving data quality
2Quantity of substance
If more labeled training data is generated to address data scarcity, then model training effectiveness improves, but noise and data quality issues may increase
Solution Approach 1:
The patent implements a reinforcement learning feedback loop where the event detection model's performance metrics on synthetic training data generate reward signals. These feedback signals guide the generative model to iteratively improve synthetic data quality by adjusting data generation parameters based on actual model performance, thereby maintaining high reliability while scaling data quantity
Solution Approach 2:
The system dynamically adjusts the characteristics of synthetic training data during the training process. The generative model adapts its data generation strategy based on feedback from the event detection model, continuously optimizing data distribution, difficulty level, and diversity to match the model's evolving capabilities and prevent overfitting
3Adaptability or versatility
If domain-specific training data is generated for specialized fields like scientific papers, then domain adaptation performance improves, but the complexity of generating accurate domain-specific data increases
Solution Approach 1:
The patent applies preliminary domain adaptation by pre-training the generative model on domain-specific corpora (e.g., scientific papers) before generating synthetic training data. This preliminary action imbues the generative model with domain knowledge, enabling it to produce authentic domain-specific data without requiring complex manual domain expert involvement during the actual data generation phase
Data Source
AI summary
In various examples, reinforcement learning techniques are used during joint training of a generative model with at least one other model. For example, a first set of training data and a second set of training data generated by the generative model are combined and used to train an event detection model. In addition, in such examples, a reward is determined based on the performance of the event detection model (e.g., an agreement between gradients of a loss function of training data and synthetic data) and used at least in part to update the parameters of the generative model.