zlacker

>>tablet+(OP)
The argument that I've heard against LLMs for code is that they create bugs that, by design, are very difficult to spot.

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.

>>jszymb+JM
This is a misunderstanding. Modern LLMs are trained with RL to actually write good programs. They aren't just spewing tokens out.

>>mindwo+TN
No, YOU misunderstand. This isn't a thing RL can fix

  https://news.ycombinator.com/item?id=44163194

  https://news.ycombinator.com/item?id=44068943

It doesn't optimize "good programs". It interprets "humans interpretation of good programs." More accurately, "it optimizes what low paid over worked humans believe are good programs." Are you hiring your best and brightest to code review the LLMs?

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.

>>godels+0S
This is just semantics. What's the difference between a "human interpretation of a good program" and a "good program" when we (humans) are the ones using it? If the model can write code that passes tests, and meets my requirements, then it's a good programmer. I would expect nothing more or less out of a human programmer.

>>mindwo+tT
> What's the difference between a "human interpretation of a good program" and a "good program" when we (humans) are the ones using it?

Correctness.

> and meets my requirements

It can't do that. "My requirements" wasn't part of the training set.

>>otabde+2V
"Correctness" in what sense? It sounds like it's being expanded to an abstract academic definition here. For practical purposes, correct means whatever the person using it deems to be correct.

> 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.