The LLM has one job, to make code that looks plausible. That's it. There's no logic gone into writing that bit of code. So the bugs often won't be like those a programmer makes. Instead, they can introduce a whole new class of bug that's way harder to debug.
https://news.ycombinator.com/item?id=44163194
https://news.ycombinator.com/item?id=44068943
Even if you do, it still optimizes tricking them. It will also optimize writing good programs, but you act like that's a well defined and measurable thing.
Correctness.
> and meets my requirements
It can't do that. "My requirements" wasn't part of the training set.
> It can't do that. "My requirements" wasn't part of the training set.
Neither are mine, the art of building these models is that they are generalisable enough that they can tackle tasks that aren't in their dataset. They have proven, at least for some classes of tasks, they can do exactly that.
> to an abstract academic definition here
> They have proven
Or did you just assume?
Yeah, I know they got good scores on those benchmarks but did you look at the benchmarks? Look at the question and look what is required to pass it. Then take a moment and think. For the love of God, take a moment and think about how you can pass those tests. Don't just take a pass at face value and move on. If you do, well I got a bridge to sell you.
[0] https://en.wikipedia.org/wiki/Correctness_(computer_science)
> In theoretical computer science, an algorithm is correct with respect to a specification if it behaves as specified.
"As specified" here being the key phrase. This is defined however you want, and ranges from a person saying "yep, behaves as specified", to a formal proof. Modern language language models are trained under RL for both sides of this spectrum, from "Hey man looks good", to formal theorem proving. See https://arxiv.org/html/2502.08908v1.
So I'll return to my original point: LLMs are not just generating outputs that look plausible, they are generating outputs that satisfy (or at least attempt to satisfy) lots of different objectives across a wide range of requirements. They are explicitly trained to do this.
So while you argue over the semantics of "correctness", the rest of us will be building stuff with LLMs that is actually useful and fun.
No they aren't. You were lied to by the hype machine industry. Sorry.
The good news is that there's a lot of formerly intractable problems that can now be solved by generating plausible output. Programming is just not one of them.
Ok. My own empirical evidence is in favour of these things being useful, and useful enough to sell their output (partly), but I'll keep in mind that I'm being lied to.
(And yes, this leap is the lie you're being sold. "LLMs are kinda useful" is not what led to the LLM trillion dollar hype bubble.)