LLM outputs are deterministic. There is no intrinsic source of randomness. Users can add randomness (temperature) to the output and modify it.
> But this article is based on a model of LLM code generation from 6 months ago
There hasn't been much change in models from 6 months ago. What happened is that we have better tooling to sift through the randomly generated outputs.
I don't disagree with your message. You are being downvoted because a lot of software developers are butt-hurt by it. It is going to force a change in the labor market for developers. In the same way the author is butt-hurt that they didn't buy Bitcoin in the very early days (as they were aware of it) and missed the boat on that.
Nit: in practice, even at temperature 0, production LLM implementations have some non-determinism. One reason is because many floating point computations are technically non-commutative even when the mathematical operation is, and the order can vary if they are carried out in parallel by the GPU. For example, see: https://www.twosigma.com/articles/a-workaround-for-non-deter...
I ran into this a bit while working on my PhD research that used LLMs for steganography. The output had to be deterministic to reverse the encoding, and it was—as long as you used the same hardware. Encoding a message on a PC and then trying to decode on a phone broke everything.
There hasn't been much change in models from 6 months ago.
I made the same claim in a widely-circulated piece a month or so back, and have come to believe it was wildly false, the dumbest thing I said in that piece.
So far the only model that showed significant advancement and differentiation was GPT-4.5. I advise to look at the problem and read GPT-4.5 answer. It'll show the difference to other "normal models" (including GPT-3.5) as it shows considerable levels of understanding.
Other normal models are now more chatty and have a bit more data. But they do not show increased intelligence.
LLM outputs are deterministic. There is no intrinsic source of randomness. Users can add randomness (temperature) to the output and modify it.
> But this article is based on a model of LLM code generation from 6 months ago
There hasn't been much change in models from 6 months ago. What happened is that we have better tooling to sift through the randomly generated outputs.
I don't disagree with your message. You are being downvoted because a lot of software developers are butt-hurt by it. It is going to force a change in the labor market for developers. In the same way the author is butt-hurt that they didn't buy Bitcoin in the very early days (as they were aware of it) and missed the boat on that.