What is Transfer Learning in the context of Language Model (LLM) customization?

What is Transfer Learning in the context of Language Model (LLM) customization?
A . It is where you can adjust prompts to shape the model’s output without modifying its underlying weights.
B . It is a process where the model is additionally trained on something like human feedback.
C . It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
D . It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.

Answer: C

Explanation:

Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related task.

Here’s a detailed explanation:

Transfer Learning: This involves taking a base model that has been pre-trained on a large dataset and fine-tuning it on a smaller, task-specific dataset.

Base Weights: The existing base weights from the pre-trained model are reused and adjusted slightly to fit the new task, which makes the process more efficient than training a model from scratch.

Benefits: This approach leverages the knowledge the model has already acquired, reducing the amount of data and computational resources needed for training on the new task.

References:

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018).A Survey on Deep Transfer Learning. In International Conference on Artificial Neural Networks.

Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).