1) "excel at a particular task"
2) "train on proprietary or sensitive data"
3) "Complex domain-specific tasks that require advanced reasoning", "Medical diagnosis based on history and diagnostic guidelines", "Determining relevant passages from legal case law"
4) "The general idea of fine-tuning is much like training a human in a particular subject, where you come up with the curriculum, then teach and test until the student excels."
Don't all these effectively inject new knowledge? It may happen through simultaneous destruction of some existing knowledge but that isn't obvious to non-technical people.
OpenAI's analogy of training a human in a particular subject until they excel even arguably excludes the possibility of destruction because we don't generally destroy existing knowledge in our minds to learn new things (but some of us may forget the older knowledge over time).
I'm a dev with hand-waving level of proficiency. I have fine-tuned self-hosted small LLMs using PyTorch. My perception of fine-tuning is that it fundamentally adds new knowledge. To what extent that involves destruction of existing knowledge has remained a bit vague.
My hand-waving solution if anyone pointed out that problem would be to 1) say that my fine-tuning data will include some of the foundational knowledge of the target subject to compensate for its destruction and 2) use a gold standard set of responses to verify the model after fine-tuning.
I for one found the article quite valuable for pointing out the problem and suggesting better approaches.
