How can transfer learning be effectively utilized to adapt pre-trained large language models to domain-specific tasks with limited data?
Abdus Sobhan
Fine-Tuning: Adjusting the pre-trained model on the limited domain-specific data. Feature Extraction: Using the pre-trained model to extract features and train a smaller model on those features. Domain Adaptation Techniques: Domain adaptation techniques apply things like domain-specific pre-training or multi-task learning to specialize the model even more. Regularization: add techniques on top of architecture such as dropout, weight decay, and early stopping to avoid overfitting. Data Augmentation: Creating more fake data to improve training
These models can often be adapted to domain-specific tasks with relatively limited training data by using transfer learning techniques to fine-tune pre-trained large language models (see, e.g.,).
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