Feature-based transfer learning and fine-tuning are two approaches commonly employed in large language models (LLMs) to adapt a pre-trained model to a specific task or dataset. Here's an overview of each approach:
Feature-based Transfer Learning:
Feature-based transfer learning involves using a pre-trained model as a fixed feature extractor, where the learned representations from the pre-trained model are used as inputs to a new model tailored for a specific task. In this approach, the weights of the pre-trained model remain frozen and are not updated during the training on the target task. Instead, additional layers or classifiers are added on top of the pre-trained model, and only these new parameters are trained on the new task. For example, one could extract contextual embeddings for each word or sentence from models like BERT or GPT and then feed those embeddings into a separate machine learning model (such as SVM, Random Forest, or a simple neural network) to perform a specific task like sentiment analysis or text classification.
Fine-Tuning:
Fine-tuning, on the other hand, involves taking the pre-trained model and continuing the training process on a new dataset specific to the target task. Unlike feature-based transfer learning, during fine-tuning, the entire model (both the pre-trained weights and any newly added layers) is updated and adjusted to better suit the new task. This method allows the model to adapt its parameters to the nuances and intricacies of the task at hand, often leading to better performance than feature extraction alone because it can exploit both the general language capabilities learned during pre-training and the specificities from the new task.
In practice, fine-tuning is more commonly used with LLMs like BERT, GPT, or RoBERTa, as it typically yields superior performance. It leverages the comprehensive understanding of language patterns and knowledge contained in these models, adjusting the network to optimize for the final objective. Fine-tuning requires careful handling to avoid overfitting, especially when the dataset for the target task is small. Techniques such as learning rate scheduling, early stopping, and employing a smaller learning rate are often employed to mitigate this.
In summary, feature-based transfer learning uses the pre-trained model as a static feature generator for a custom model built for a specific task, while fine-tuning updates the entire pre-trained model on the target task data to benefit from both the pre-existing knowledge and the new task-specific information.
