Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yosuf, Naren Ramakrishnan·June 20, 2024
Summary
The paper investigates the need for targeted regularization in fine-tuning large language models for better transfer learning. It focuses on task-sensitive parameters and their role in the pretraining loss landscape using Fisher information. The authors introduce guided dropout, a task- and architecture-agnostic technique that improves generalization, especially in low-data scenarios. By connecting Fisher information to the loss landscape, the study finds that perturbing high Fisher-score parameters affects the loss geometry negatively. Guided dropout, demonstrated with BERT and dropout as L2 regularization, consistently outperforms standard methods, particularly in scenarios with limited data. The research contributes to enhancing fine-tuning efficiency for diverse tasks by mitigating overfitting and optimizing the loss landscape.
Introduction
Background
Emergence of large language models and their transfer learning potential
Challenges in fine-tuning for diverse tasks with limited data
Objective
To address the need for task-specific regularization in fine-tuning
Investigate the role of task-sensitive parameters in the pretraining loss landscape
Introduce guided dropout as a novel regularization technique
Method
Data Collection
Selection of large language models (e.g., BERT)
Diverse datasets for pretraining and fine-tuning tasks
Data Preprocessing
Preprocessing techniques for model input and output
Handling class imbalance and data augmentation (if applicable)
Loss Landscape Analysis
Calculation of Fisher information for task-sensitive parameters
Connection between Fisher information and loss geometry
Guided Dropout
Technique Description
Task- and architecture-agnostic approach
Integration with BERT and dropout as L2 regularization
Implementation
Dropout modification to target high Fisher-score parameters
Integration into fine-tuning process
Evaluation
Performance comparison with standard regularization methods
Low-data scenarios as a primary focus
Results
Impact of guided dropout on model generalization
Improvement in transfer learning efficiency
Reduction in overfitting during fine-tuning
Discussion
Interpretation of Fisher information in the context of fine-tuning
Limitations and potential extensions of guided dropout
Implications for future research on model optimization
Conclusion
Summary of key findings
Contribution to the field of fine-tuning and transfer learning
Recommendations for practitioners and future directions
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features