> LLMs are much easier to understand when you think of them in terms of human psychology
Are they? You can reasonably expect from a human that they will learn from their mistake, and be genuinely sorry about it which will motivate them to not repeat the same mistake in the future. You can't have the same expectation from an LLM.
The only thing you should expect from an LLM is that its output is non-deterministic. You can expect the same from a human, of course, but you can fire a human if they keep making (the same) mistake(s).
While the slowness of learning of all ML is absolutely something I recognise, what you describe here:
> You can reasonably expect from a human that they will learn from their mistake, and be genuinely sorry about it which will motivate them to not repeat the same mistake in the future.
Wildly varies depending on the human.
Me? I wish I could learn German from a handful of examples. My embarrassment at my mistakes isn't enough to make it click faster, and it's not simply a matter of motivation here: back when I was commuting 80 minutes each way each day, I would fill the commute with German (app) lessons and (double-speed) podcasts. As the Germans themselves will sometimes say: Deutsche Sprache, schwere Sprache.
There's been a few programmers I've worked with who were absolutely certain they knew better than me, when they provably didn't.
One, they insisted a start-up process in a mobile app couldn't be improved, I turned it from a 20 minute task to a 200ms task by the next day's standup, but they never at any point showed any interest in improving or learning. (Other problems they demonstrated included not knowing or caring how to use automated reference counting, why copy-pasting class files instead of subclassing cannot be excused by the presence of "private" that could just have been replaced with "public", and casually saying that he had been fired from his previous job and blaming this on personalities without any awareness that even if true he was still displaying personality conflicts with everyone around him).
Another, complaining about too many views on screen, wouldn't even let me speak, threatened to end the call when I tried to say anything, even though I had already demonstrated before the call that even several thousand (20k?) widgets on-screen at the same time would still run at 60fps and they were complaining about order-of 100 widgets.
But the difference is, all humans are capable of it, whether or not they have the tools to exercise that capability in any given situation.
No LLM is capable of it*.
* Where "it" is "recognizing they made a mistake in real time and learning from it on their own", as distinct from "having their human handlers recognize they made 20k mistakes after the fact and running a new training cycle to try to reduce that number (while also introducing fun new kinds of mistakes)".
> When they don't have the tools to exercise that capability, it's a distinction without any practical impact.
It has huge practical impact.
If a human doesn't currently have the tools to exercise the capability, you can help them get those.
This is especially true when the tools in question are things like "enough time to actually think about their work, rather than being forced to rush through everything" or "enough mental energy in the day to be able to process and learn, because you're not being kept constantly on the edge of a breakdown." Or "the flexibility to screw up once in a while without getting fired." Now, a lot of managers refuse to give their subordinates those tools, but that doesn't mean that there's no practical impact. It means that they're bad managers and awful human beings.
An LLM will just always be nondeterministic. If you're the LLM "worker"'s "boss", there is nothing you can do to help it do better next time.
> they can sometimes recognise they made a mistake and correct it.
...And other times, they "recognize they made a mistake" when they actually had it right, and "correct it" to something wrong.
"Recognizing you made a mistake and correcting it" is a common enough pattern in human language—ie, the training corpus—that of course they're going to produce that pattern sometimes.
A generic "you" might, I personally don't have that skill.
But then, I've never been a manager.
> An LLM will just always be nondeterministic.
This is not relevant, humans are also nondeterministic. At least practically speaking, theoretically doesn't matter so much as we can't duplicate our brains and test us 10 times on the same exact input without each previous input affecting the next one.
> If you're the LLM "worker"'s "boss", there is nothing you can do to help it do better next time.
> "Recognizing you made a mistake and correcting it" is a common enough pattern in human language—ie, the training corpus—that of course they're going to produce that pattern sometimes.
Yes. This means that anthropomorphising them leads to a useful prediction.
For similar reasons, I use words like "please" and "thank you" with these things, even though I don't actually expect these models to have constructed anything resembling a real human emotional qualia within them — humans do better when praised, therefore I have reason to expect that any machine that has learned to copy human behaviour will likely also do better when praised.
> This is not relevant, humans are also nondeterministic.
I mean, I suppose one can technically say that, but, as I was very clearly describing, humans both err in predictable ways, and can be taught not to err. Humans are not nondeterministic in anything like the same way LLMs are. LLMs will just always have some percentage chance of giving you confidently wrong answers. Because they do not actually "know" anything. They produce reasonable-sounding text.
> Yes there is
...And no matter how well you engineer your prompts, you cannot guarantee that the LLM's outputs will be any less confidently wrong. You can probably make some improvements. You can hope that your "prompt engineering" has some meaningful benefit. But not only is that nowhere near guaranteed, every time the models are updated, you run a very high risk that your "prompt engineering" tricks will completely stop working.
None of that is true with humans. Human fallibility is wildly different than LLM fallibility, is very-well-understood overall, and is highly and predictably mitigable.
Yes, hugely. Just assume it's like a random person from some specific pool with certain instructions you've just called on the phone. The idea that you then call a fresh person if you call back is easy to understand.
I'm genuinely wondering if your parent comment is correct and the only reason we don't see the behaviour you describe, IE, learning and growth is because of how we do context windows, they're functionally equivalent to someone who has short term memory loss, think Drew Barrymore's character or one of the people in that facility she ends up in in the film 50 first dates.
Their internal state moves them to a place where they "really intend" to help or change their behaviour, a lot of what I see is really consistent with that, and then they just, forget.
I think it's a fundamental limitation of how context works. Inputting information as context is only ever context; the LLM isn't going to "learn" any meaningful lesson from it.
You can only put information in context; it struggles learning lessons/wisdom
Not only, but also. The L in ML is very slow. (By example count required, not wall-clock).
On in-use learning, they act like the failure mode of "we have outsourced to a consultant that gives us a completely different fresh graduate for every ticket, of course they didn't learn what the last one you talked to learned".
Within any given task, the AI have anthropomorphised themselves because they're copying humans' outputs. That the models model the outputs with only a best-guess as to the interior system that generates those outputs, is going to make it useful, but not perfect, to also anthropomorphise the models.
The question is, how "not perfect" exactly? Is it going to be like early Diffusion image generators with the psychological equivalent of obvious Cronenberg bodies? Or the current ones where you have to hunt for clues and miss it on a quick glance?
Obviously they aren't actually people so there are many low hanging differences. But consider this: Using words like please and thank you get better results out of LLMs. This is completely counterintuitive if you treat LLMs like any other machine, because no other machine behaves like that. But it's very intuitive if you approach them with thinking informed by human psychology.
> You can reasonably expect from a human that they will learn from their mistake, and be genuinely sorry about it which will motivate them to not repeat the same mistake in the future.
Are they? You can reasonably expect from a human that they will learn from their mistake, and be genuinely sorry about it which will motivate them to not repeat the same mistake in the future. You can't have the same expectation from an LLM.
The only thing you should expect from an LLM is that its output is non-deterministic. You can expect the same from a human, of course, but you can fire a human if they keep making (the same) mistake(s).