Transfer learning in the context of Large Language Models (LLMs) involves leveraging a pre-trained model on a new task or domain with minimal additional training. This approach takes advantage of the knowledge the LLM has already acquired through extensive pre-training on vast datasets. Here are several transfer learning techniques commonly used with LLMs:
Fine-Tuning:
- The most common transfer learning technique is fine-tuning, where the entire pre-trained model is fine-tuned on a specific downstream task using a smaller, task-specific dataset. During fine-tuning, the weights of the LLM are updated to better fit the new task. This can be done for tasks like text classification, named entity recognition, question answering, and more.
Feature Extraction (Frozen Model):
- The LLM can be used as a feature extractor where the pre-trained weights are kept frozen, and only the additional task-specific layers added on top are trained. This is akin to using the LLM as a powerful embedding generator, with downstream models learning from these embeddings.
Prompting and Few-Shot Learning:
- Prompting involves formulating the task such that the model generates the required output as a continuation of a given input prompt. This has become increasingly popular with models like GPT-3 that can perform tasks in a few-shot or zero-shot learning manner, where the model is provided with examples or descriptions of the task as part of the input.
Adapters:
- Adapter modules are small trainable neural networks inserted within each layer of a pre-trained model. Only the adapters are trained (with the rest of the model parameters frozen), making this a parameter-efficient transfer learning method. The adapters learn task-specific representations while leveraging the pre-trained model's knowledge.
Multi-Task Learning:
- Pre-trained models can be further trained on a combination of several tasks simultaneously to improve generalization. This is not strictly transfer learning as it involves simultaneous adaptation to multiple tasks, but it is a related approach to leverage pre-existing knowledge for improved performance on several tasks.
Domain-Adaptive Pre-Training (DAPT):
- For tasks that fall into specific domains (like medical or legal text), the pre-trained LLM can undergo an additional pre-training phase using in-domain data. This helps the model adapt to the domain's language nuances before being fine-tuned on the final task.
Task-Adaptive Pre-Training (TAPT):
- Similar to DAPT, but more focused, TAPT exposes the model to task-specific data before fine-tuning. This can involve unsupervised pre-training on data that resembles the downstream task's structure or content, which can help with tasks that deviate significantly from the data used during the original pre-training.
Cross-lingual Transfer:
- For models pre-trained on multiple languages (like mBERT and XLM), transfer learning can also involve transferring knowledge from one language to another. This is useful when a task has ample data in one language but limited data in another. The model, pre-trained multilingually, can be fine-tuned on the high-resource language and then applied to the low-resource language, leveraging cross-lingual transfer.
Each of these techniques utilizes the knowledge captured by the LLM during its initial pre-training phase to varying extents, with some methods updating most or all of the model's parameters, and others only a small fraction. The choice of technique often depends on the downstream task, the amount of available task-specific data, and the computing resources at hand.