LLMs have a temperature parameter. At zero temperature they are deterministic: they always choose the most likely next token at each step based on what came before and the model weights, and they will always generate the same output given the same input.

As you raise the temperature they will start (pseudo)randomly choosing tokens other than the single most likely token (though that one will still be the most likely to be chosen). It turns out this is almost always better than zero temperature, which has a tendency to get caught in repetitive loops. I imagine all the frontier labs have spent thousands (millions?) of CPU hours tuning the temperature parameters on their models for optimal performance.

I didn’t claim that LLMs are people or that they reason.

If the behavior of the llm is the same as the behavior of reasonable people then the behavior of the llm is reasonable, regardless of how black of a box they generate tokens out of.

Reasonable people will generate divergent specs for the same prompt. Thus it is reasonable for an LLM to generate divergent specs out of the same prompt.

Edit: I use “reasonable” here in the legal sense of the “reasonable person” standard, not to imply any reasoning process.

it's an analogy, it didnt fall on its face at all. it's just a comparison to highlight the point being made was nonsensical. example: you're just a next action generator controlled by trillions of cells and subconscious dna-based behavior. a black box.

LLMs do reason (they just sometimes don't reason well).

I assure you I've met many devs and "engineers" that reason less than LLMs, and are black boxes, especially in terms of the code they write.

They very obviously reason.

The guessing of a LLM for a vague prompt is better than the one of your average developer.

A prompt like "write these two files on disk" will very likely make the LLM do some sort of an atomic write/swap operation, unlike the average developer which will just write the two files and maybe later encounter a race condition bug. You can argue the LLM output is overkill, but it will also be more robust on average.

> We expect our machines to behave in predictable ways.

I expect LLMs to produce randomly varying output. Maybe it's the thousands of hours I spent doing monte carlo simulations for my PhD.

> This is one of the best arguments against using LLMs I've seen.

> It reduces to the classic argument- at the point where you've described a problem and solution in sufficient detail to be confident in the results, you've invented a programming language.

I'm not an LLM true believer, but I use codex for various small tasks and it often (not always) does a thoroughly decent job. Yesterday I gave it a pretty vague request to set up a new Home Assistant dashboard and it handled it just fine--I told it what I wanted to see but it figured out itself which helper variables it would need to set up to realize that vision and wrote all the config for it.

I probably could have done it in 15 minutes if I was familiar with Home Assistant's yaml configuration schema and all, but I'm not so it probably would have taken me closer to an hour. Asking codex took me 30 seconds and it did just fine.

I am skeptical that LLM's are going to kill all white collar jobs or whatever anytime soon. Not being able to truly learn things is an issue. Reality has a surprising amount of detail[1], and while codex does well at things like writing Home Assistant configs and setting up a Minecraft server, where there are thousands of examples online of how to do it, when I've asked it to do some more esoteric things it has sometimes failed spectacularly. I don't think having the LLM keep notes and then read them back (filling up the context window) is a real solution here.

[1] http://johnsalvatier.org/blog/2017/reality-has-a-surprising-...