Trust me I am one of the people absolutely baffled and pleasantly surprised by all the new things these LLMs can do. There are plenty of potential in this tech and I am not so scared to not acknowledge that.
But what I am asking is a core feature of these models. The transformer is what make LLMs possible but it is by design static after it was trained. You can't simply install another pipeline or wrap it in a program and fix this problem. From what I understand, RNN isthe self improving type of AI and it was supplanted by the transformer type and one of the key differences between them is the ability to do learning from its own output. Transformer model sacrificed that for the ability to process a large amount of data in parallel. And that is by design. You can't change it as far as I know.
I actually don't know that much so that is why I am asking if what I said was wrong. Also, maybe someone will make a new model merging both transformer and RNN. But what I am saying is right now, the type of LLMs we have probably can't learn no matter how sophisticated its training is or how many parameters it is built with.
But what I am asking is a core feature of these models. The transformer is what make LLMs possible but it is by design static after it was trained. You can't simply install another pipeline or wrap it in a program and fix this problem. From what I understand, RNN isthe self improving type of AI and it was supplanted by the transformer type and one of the key differences between them is the ability to do learning from its own output. Transformer model sacrificed that for the ability to process a large amount of data in parallel. And that is by design. You can't change it as far as I know.
I actually don't know that much so that is why I am asking if what I said was wrong. Also, maybe someone will make a new model merging both transformer and RNN. But what I am saying is right now, the type of LLMs we have probably can't learn no matter how sophisticated its training is or how many parameters it is built with.