I agree with you that the headline really needs to be qualified with these details. So there's an aspect of being unsurprising here, because that particular set of details is exactly where LLMs perform very well.
But I think it's still an interesting result, because related and similar tasks are everywhere in our modern world, and they tend to have high importance in both business and the public sector, and the older generation of machine learning techniques for handling these tasks we're both sophisticated and to the point where very capable and experienced practitioners might need an R&D cycle just to conclude if the problem was solvable with the available data up to the desired standard.
LLM's represent a tremendous advancement in our ability as a society to deal with these kinds of tasks. So yes, it's a limited range of specific tasks, and success is found within a limited set of criteria, but it's a very important tasks and enough of those criteria are met in practice that I think this result is interesting and generalizable.
That doesn't mean we should fire all of our data scientists and let junior programmers just have at it with the LLM, because you still need to put together a good day to say, makes sense of the results, and iterate intelligently, especially given that these models tend to be expensive to run. It does however mean that existing data teams must be open to adopting LLMs instead of traditional model fitting.
But I think it's still an interesting result, because related and similar tasks are everywhere in our modern world, and they tend to have high importance in both business and the public sector, and the older generation of machine learning techniques for handling these tasks we're both sophisticated and to the point where very capable and experienced practitioners might need an R&D cycle just to conclude if the problem was solvable with the available data up to the desired standard.
LLM's represent a tremendous advancement in our ability as a society to deal with these kinds of tasks. So yes, it's a limited range of specific tasks, and success is found within a limited set of criteria, but it's a very important tasks and enough of those criteria are met in practice that I think this result is interesting and generalizable.
That doesn't mean we should fire all of our data scientists and let junior programmers just have at it with the LLM, because you still need to put together a good day to say, makes sense of the results, and iterate intelligently, especially given that these models tend to be expensive to run. It does however mean that existing data teams must be open to adopting LLMs instead of traditional model fitting.