Fine-tuning versus Re-training

🤖 ai💻 cs🤖 ml

🧠 intelligence

Would fine-tuning allow these new good behaviours to be properly embedded to make the model more 'intelligent' and predict better next tokens, or is the only way to become more 'intelligent' to retrain the model itself?

Disclosure: Google Gemini AI produced this answer.

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 behaviours 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.