The concept of training a Large Language Model (LLM) to "store" a specific context typically involves incorporating relevant information into the model during its training phase, so that the context becomes part of the model's internal knowledge. This can be done using various methods:
Continual Pre-Training or Domain-Adaptive Pre-Training (DAPT):
- After the generic pre-training on a large corpus, you can continue to train (i.e., adaptively pre-train) the model on a dataset that contains the specific context you want the model to store. This process familiarizes the LLM with the context and patterns related to that domain or information.
Task-Adaptive Pre-Training (TAPT):
- Similar to DAPT, TAPT involves additional training on data that closely resembles the task at hand. This can include data that contains the specific context or situation that you want the LLM to understand and "remember."
Fine-Tuning with Contextual Data:
- You can fine-tune the LLM on a dataset that includes examples embedding the specific context. This process ensures the model learns to associate certain responses or behavior with that context.
Injecting Knowledge as Parameters:
- Some methods involve injecting external knowledge into an LLM through the model's parameters. This can be achieved with knowledge distillation techniques or structured approaches to encoding knowledge into the weights of the neural network.
Custom Prompt Design:
- Prompt engineering involves carefully designing prompts that include specific context or instructions, which direct the model to utilize or recall particular information when generating responses or carrying out tasks.
While these methods can train an LLM to effectively utilize specific context during inference, it's important to note that traditional LLMs don't "store" information in the same way a database would. Instead, they aggregate patterns and statistics over their training data and use this to generate predictions or responses related to the provided context. They generalize based on the context present in the input text, rather than recalling stored information.
Moreover, with the advent of models like GPT-3 and "prompt programming," researchers have explored dynamic ways to provide context to a model at runtime without the need for additional training. By crafting the prompt to include the necessary context, the model can use this information to inform its output, effectively leveraging the context as part of the task without having stored it during training. However, such methods rely on the model already having been pre-trained on sufficiently diverse data to recognize and utilize the provided context.