Fine-tuning versus Re-training

#ai #cs

Fine-tuning:

What it does: Adjusts the existing parameters of a pre-trained LLM based on new data. Think of it as refining the model's existing knowledge and abilities.

Effect on intelligence:

Can definitely improve the model's ability to predict the next token in a way that aligns with your desired behaviors.

Can make the model more accurate, relevant, and consistent within the scope of the fine-tuning data.

However, it might not fundamentally change the model's core understanding or reasoning abilities.

Analogy: Like teaching a talented musician a new song. They still have the same fundamental skills, but they can now play that specific song better.

Retraining:

What it does: Involves training the LLM from scratch (or a very early stage) using a new dataset, potentially with a different architecture or training objective.

Effect on intelligence:

Has the potential to more significantly alter the model's core capabilities, including its reasoning, understanding, and generation of novel ideas.

Can lead to a more profound shift in the model's overall "intelligence."

Analogy: Like providing a musician with years of new training and experiences, potentially leading to a transformation in their musical style and abilities.

Which is right for you?

Fine-tuning is often a good starting point: It's less resource-intensive than retraining and can be effective for embedding specific behaviors and improving performance on targeted tasks.

Retraining might be necessary for larger shifts: If you want to see a more fundamental change in your LLM's intelligence or want it to generalize to a much broader range of tasks, retraining might be required.