4 ms·

> 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

by bojan 10mo ago

> 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).

Folcon 10mo ago
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.
- Libidinalecon 10mo ago
  No, the idea is just stupid. I just don't understand how anyone who actually uses the models all the time can think this. The current models themselves can even explain what a stupid idea this is.
- ben_w 10mo ago
  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?
- knollimar 10mo ago
  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
scotty79 10mo ago
> 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. Have you talked to a human? Like, ever?
- Xss3 10mo ago
  Have you?
ben_w 10mo ago
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.
- danaris 10mo ago
  > Wildly varies depending on the human. Sure. And the situation. 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)".
  - 10mo ago
IanCal 10mo ago
> Are they? 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.
mikkupikku 10mo ago
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.

by bojan 10mo ago

Folcon 10mo ago

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.

Libidinalecon 10mo ago
No, the idea is just stupid. I just don't understand how anyone who actually uses the models all the time can think this. The current models themselves can even explain what a stupid idea this is.
ben_w 10mo ago
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?
knollimar 10mo ago
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

scotty79 10mo ago

> 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. Have you talked to a human? Like, ever?

Xss3 10mo ago
Have you?

ben_w 10mo ago

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.

danaris 10mo ago
> Wildly varies depending on the human. Sure. And the situation. 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)".
- 10mo ago

IanCal 10mo ago

> Are they? 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.

mikkupikku 10mo ago

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.