Outside of pure interest - how these LLMs are working, the utility/impact of understanding them would be to be able to control them - how to remove capabilities you don't want them to have (safety), or perhaps even add capabilities, or just steer their behavior in some desirable way.
Pretty much everything about NNs is engineering - it's basically an empirical technology, not one that we have much theoretical understanding of outside of the very basics.
> Pretty much everything about NNs is engineering - it's basically an empirical technology, not one that we have much theoretical understanding of outside of the very basics.
This pretty much answers the question some have asked: “why are the world’s preeminent mathematicians not working on AI if AGI will solve everything eventually anyway?”.
At least for now, the skills required to make progress in AI (machine learning as it largely is now) are those of an engineer rather than a mathematician.
Pretty much everything about NNs is engineering - it's basically an empirical technology, not one that we have much theoretical understanding of outside of the very basics.