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This. I get why people have started using LLMs for this and I think it's great in theory, but the black box nature and possibility of hallucination makes it a n
by voidhorse 2y ago
This. I get why people have started using LLMs for this and I think it's great in theory, but the black box nature and possibility of hallucination makes it a non starter for me. Having the LLM generate scripts which you can then validate for correctness seems more plausible.
I also worry that this approach will lead to a sort of further reification of data science. While things have already trended this way, data science is not about applying a few routine formulas to a data set. Done properly, it is far more exploratory and all about building an understanding of the unique properties and significance of a particular data set. I worry the use of these tools will greatly reduce the exploratory phase and lead to analyses that simply confirm biases or typical conclusions rather than yielding new insight.
- huijzer 2y agoThe output is not black box. I always see myself as responsible for the output. The models give hints.
- daveguy 2y agoDefinitely the right way to approach this. You already need to know what you're doing (for validation and error checking), but if you do it can be faster. As long as P != NP the validation is faster than coming up with the solution. My only concern is how far away from a "good" solution is the quick LLM + check vs expert solution. It may be worth using human expertise in 2 weeks than validated LLM solution in 2 hours. (And i'd question good validation of traditionally 2 week work in 2 hours.) There's going to be a lot of moving fast and breaking things coming. Hopefully less breaking than moving.