4 ms·

The rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand. These models can do a lot of things

by Jensson 2mo ago

The rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand. These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.

gbnwl 2mo ago
Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?
- Jensson 2mo ago
  They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that. For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new skills to them, so they will continue to get more useful.
  - deleted 2mo ago
    [deleted]
  - p2detar 2mo ago
    In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.
    - Jensson 2mo ago
      Humans learn to be a next token predictor as a kid when they learn to speak, an LLM cannot learn to be a next token predictor or anything of the sort, we have no clue how you could have an LLM learn human language just based on a thousands conversations with a human. You don't see how that is very different? For an LLM to be as smart as a human it has to be able to learn like a human. Like you don't evaluate how smart a human is based on how much he knows, you evaluate it based on how fast he learns. And LLM are so bad at learning its ridiculous, they lack that part of the brain that lets humans be smart and learn so fast and easily.
      - fsflover 2mo ago
        > For an LLM to be as smart as a human it has to be able to learn like a human. "For a plane to fly as well as a bird it has to be able to flap its wings". "For a submarine to swim as well as a fish it has to be as light as fish".
        27183 2mo ago
        > "For a plane to fly as well as a bird it has to be able to flap its wings". > "For a submarine to swim as well as a fish it has to be as light as fish". These are false equivalences. The post you're responding to defined intelligence as learning rate. LLMs unequivocally do not learn. You can disagree with OP or agree, but what you have done is out of bounds. You're implicitly claiming that LLMs learn, albeit differently from humans. This is categorically false, unless you count training as some kind of "learning". That's of course absurd, almost no user of an LLM also trains it. Instead they rely on queries submitted to pre-trained LLMs ("inference"). And don't bother yapping about context windows, it's just not anything like learning.
        fsflover 2mo ago
        > The post you're responding to defined intelligence as learning rate. How is the learning method or rate related to intelligence? LLMs learn during the training, much faster than any human. Yes, they drastically slow down their learning afterwards, but they still can learn a bit from an uploaded document or a web site. And in the end they may be better at intelligence than any human, depending on the task and time given. I though intelligence is not about history but current performance. How is this different than my examples, in which a possibility to fly is judged by (learning to) flapping the wings and not by the actual result? > And don't bother yapping about context windows, it's just not anything like learning. That's debatable, but I don't see how it's relevant here. You can ignore that part of my reply above, and the argument will remain unchanged. Nevertheless, I don't understand how this not learning. Without it, no meaningful intelligent task can really be performed. LLM/human must learn the relevant bits from current situation in order to answer meaningfully. Often it requires to actually acquire new knowledge like reading a new piece of text unknown before. Feel free to link to a relevant discussion for me if you find this boring and settled.
- inigyou 2mo ago
  Somewhat. Yes, but not because they are useful.
cindyllm 2mo ago
[dead]
diydsp 2mo ago
Much of an LLM's capability comes from the structure encoded in its learned representations. The probabilistic outputs are primarily a way of expressing uncertainty and generating fluent text, while compression during training is what forces the model to discover that underlying structure.
- Jensson 2mo ago
  > Much of an LLM's capability comes from the structure encoded in its learned representations And thats encoded as a set of next token predictions. So the way to see how reliably it solves a problem is to look at the chain of predictions, and see where it is unreliable at finding the next spot, or where it always fails and you need to add that link to the dataset to train it. This isn't magic, today we understand pretty well how to add new skills to LLM, and the better this is understood the faster progress will be. This also means that if a context doesn't have any good predictions, it will produce a dumb prediction for that context. This results in these bad outcomes, because currently LLM doesn't have a map for where predictions are good or bad.
vouwfietsman 2mo ago
It can be a token predictor and still tell me exactly how my life will proceed from now until the indefinite future, or be the most intelligent conversational entity you have ever witnessed. The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time. To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.
- Jensson 2mo ago
  The "dumb" part comes from how it behaves in contexts where it lacks a lot of data, or where the data is skewed. Since they are tuned to give a prediction anyway and just make something up since sometimes those made up things are useful they will produce dumb results. So people call them dumb since like dumb people they make strong statements about things they don't understand. And it doesn't matter how much smart things you encode them with, they will keep making strong statements about things they don't understand until they are fundamentally changed. But since LLM are very smart about things where they have extensive data they can still be used to reliable solve many problems and probably in the future where we understand that better almost completely replace most lawyer and doctors work etc, because a lot of what a frontline doctor or basis lawyer work is very repetitive and can be encoded with billions of examples and decision paths into an expert system framework the LLM will follow. So people say LLM are dumb since LLM will always keep making dumb statements. This is the same way we call Elon Musk dumb for making a lot of dumb statements, he is a smart guy but he makes dumb statements so her is dumb.
  - vouwfietsman 2mo ago
    > they will keep making strong statements about things they don't understand until they are fundamentally changed If ever there was a human quality. Also, your explanation of "dumb" is really favoring the anti-llm side, and its a very generous interpretation. I suspect what is much more likely meant, is that token predictors cannot be smart, not now nor in the future after improvements, because they are token predictors and predicting tokens is not how intelligence works. All of this is of course unfounded, and hidden behind the word "dumb".

by Jensson 2mo ago

gbnwl 2mo ago

Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?

Jensson 2mo ago
They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that. For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new skills to them, so they will continue to get more useful.
- deleted 2mo ago
  [deleted]
- p2detar 2mo ago
  In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.
  - Jensson 2mo ago
    Humans learn to be a next token predictor as a kid when they learn to speak, an LLM cannot learn to be a next token predictor or anything of the sort, we have no clue how you could have an LLM learn human language just based on a thousands conversations with a human. You don't see how that is very different? For an LLM to be as smart as a human it has to be able to learn like a human. Like you don't evaluate how smart a human is based on how much he knows, you evaluate it based on how fast he learns. And LLM are so bad at learning its ridiculous, they lack that part of the brain that lets humans be smart and learn so fast and easily.
    - fsflover 2mo ago
      > For an LLM to be as smart as a human it has to be able to learn like a human. "For a plane to fly as well as a bird it has to be able to flap its wings". "For a submarine to swim as well as a fish it has to be as light as fish".
      - 27183 2mo ago
        > "For a plane to fly as well as a bird it has to be able to flap its wings". > "For a submarine to swim as well as a fish it has to be as light as fish". These are false equivalences. The post you're responding to defined intelligence as learning rate. LLMs unequivocally do not learn. You can disagree with OP or agree, but what you have done is out of bounds. You're implicitly claiming that LLMs learn, albeit differently from humans. This is categorically false, unless you count training as some kind of "learning". That's of course absurd, almost no user of an LLM also trains it. Instead they rely on queries submitted to pre-trained LLMs ("inference"). And don't bother yapping about context windows, it's just not anything like learning.
        fsflover 2mo ago
        > The post you're responding to defined intelligence as learning rate. How is the learning method or rate related to intelligence? LLMs learn during the training, much faster than any human. Yes, they drastically slow down their learning afterwards, but they still can learn a bit from an uploaded document or a web site. And in the end they may be better at intelligence than any human, depending on the task and time given. I though intelligence is not about history but current performance. How is this different than my examples, in which a possibility to fly is judged by (learning to) flapping the wings and not by the actual result? > And don't bother yapping about context windows, it's just not anything like learning. That's debatable, but I don't see how it's relevant here. You can ignore that part of my reply above, and the argument will remain unchanged. Nevertheless, I don't understand how this not learning. Without it, no meaningful intelligent task can really be performed. LLM/human must learn the relevant bits from current situation in order to answer meaningfully. Often it requires to actually acquire new knowledge like reading a new piece of text unknown before. Feel free to link to a relevant discussion for me if you find this boring and settled.
inigyou 2mo ago
Somewhat. Yes, but not because they are useful.

cindyllm 2mo ago

[dead]

diydsp 2mo ago

Much of an LLM's capability comes from the structure encoded in its learned representations. The probabilistic outputs are primarily a way of expressing uncertainty and generating fluent text, while compression during training is what forces the model to discover that underlying structure.

Jensson 2mo ago
> Much of an LLM's capability comes from the structure encoded in its learned representations And thats encoded as a set of next token predictions. So the way to see how reliably it solves a problem is to look at the chain of predictions, and see where it is unreliable at finding the next spot, or where it always fails and you need to add that link to the dataset to train it. This isn't magic, today we understand pretty well how to add new skills to LLM, and the better this is understood the faster progress will be. This also means that if a context doesn't have any good predictions, it will produce a dumb prediction for that context. This results in these bad outcomes, because currently LLM doesn't have a map for where predictions are good or bad.

vouwfietsman 2mo ago

It can be a token predictor and still tell me exactly how my life will proceed from now until the indefinite future, or be the most intelligent conversational entity you have ever witnessed. The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time. To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.

Jensson 2mo ago
The "dumb" part comes from how it behaves in contexts where it lacks a lot of data, or where the data is skewed. Since they are tuned to give a prediction anyway and just make something up since sometimes those made up things are useful they will produce dumb results. So people call them dumb since like dumb people they make strong statements about things they don't understand. And it doesn't matter how much smart things you encode them with, they will keep making strong statements about things they don't understand until they are fundamentally changed. But since LLM are very smart about things where they have extensive data they can still be used to reliable solve many problems and probably in the future where we understand that better almost completely replace most lawyer and doctors work etc, because a lot of what a frontline doctor or basis lawyer work is very repetitive and can be encoded with billions of examples and decision paths into an expert system framework the LLM will follow. So people say LLM are dumb since LLM will always keep making dumb statements. This is the same way we call Elon Musk dumb for making a lot of dumb statements, he is a smart guy but he makes dumb statements so her is dumb.
- vouwfietsman 2mo ago
  > they will keep making strong statements about things they don't understand until they are fundamentally changed If ever there was a human quality. Also, your explanation of "dumb" is really favoring the anti-llm side, and its a very generous interpretation. I suspect what is much more likely meant, is that token predictors cannot be smart, not now nor in the future after improvements, because they are token predictors and predicting tokens is not how intelligence works. All of this is of course unfounded, and hidden behind the word "dumb".