3 ms·

To me predicting the next token is obviously not how humans think. If I ask you to envision a green triangle and a red square next to each other, and then swap

by ffwd 3y ago

To me predicting the next token is obviously not how humans think. If I ask you to envision a green triangle and a red square next to each other, and then swap the shapes but keep the colors in the same locations, and answer what color is the triangle now, you say the triangle is red, but you do so because you envisioned the triangle swapping places and did the mental steps etc. An LLM if even answering correctly, is statistically answering based on billions of lines of text + rlhf and all of this, I highly doubt there is a mental model of the world, but rather a large set of constraints in the probabilities which leads to the resulting answer. The reasoning ability is a secondary effect of the probabilities which is why it's hard to make it so every probability is correct for every answer I think. And regarding OP about hallucinating vs confabulating. To me hallucinating is a fine word for it, because it is filling in a gap or there aren't enough constraints in the model/data/tuning to account for that specific answer that it gave that was incorrect. Hence it "hallucinates" something in the gap. The real power of LLM's is that it seems to accumulate these 'constraints' (generalization), so that with the right model, it should be able to answer more and more prompts correctly, which is kind of amazing. Confabulation works too but is a little more high level IMO.

klik99 3y ago
I'm not entirely sure if the mental model is somehow a layer deeper than the prediction that humans do. I used to believe it, and still use it as shorthand, but these days I'm not sure it's accurate. The triangle example doesn't prove it because our predictive model could also say "hey, things don't just change color and shape like that, they need to move". It's similar to how an LLM can be more accurate with math when asked to step through and reason through it's logic - by stepping through the individual steps it can create a larger system. The thing that made me question if a "mental model" is at the base of human cognition - people who do those memory competitions, the clear winning strategy is the memory palace, or imagining walking through a house where each room is another number - they have to build step by step memory, it's not like an SQL database where they can just SELECT from random. Another one was the insight from GTD that if you remember 7 things to do, you're always repeating those 7 things to yourself to keep them in active memory. There's a strong argument that the mental model is derivative of a predictive model in the human brain, and we can just appear to have a mental model since we have an internal dialogue that runs so fast in the background that even we rarely recognize it. (anyone who has kept a steady meditation or similar practice should be familiar with it)
- ffwd 3y ago
  Well I can't say any of this for sure but I want to say upfront, I think llm's can in theory do a lot (not sure if most) of the computations a human can do, but it's important to realize, imo, it's not actually stepping through the steps in the way humans are. When we give complicated step by step prompts and so on, it only means it's creating new constraints for what the probability of the next token is (from what exists in the data/model). If the data/model doesn't contain the data needed to produce the desired result, or the data that it was trained does not have examples that can generalize (but not be specific to) the desired result then it can't produce it. That's the difference between humans and llm's imo. We can generalize any "computation" we have to any other "desired output" we want, by thinking about it, while llm's aren't at least not now, so general that they use low level representations of all the 'objects' we can prompt about. Like humans can reason about the objects and things in our mind almost infinitely and recursively while also retaining all the physical realities and facts of those objects, while an llm is limited in this regard. Doesn't mean it can't in theory, there is some weird generalization going on as far as I can tell, but it feels like it's going to need a lot more data or something to do it.

by ffwd 3y ago

klik99 3y ago

I'm not entirely sure if the mental model is somehow a layer deeper than the prediction that humans do. I used to believe it, and still use it as shorthand, but these days I'm not sure it's accurate. The triangle example doesn't prove it because our predictive model could also say "hey, things don't just change color and shape like that, they need to move". It's similar to how an LLM can be more accurate with math when asked to step through and reason through it's logic - by stepping through the individual steps it can create a larger system. The thing that made me question if a "mental model" is at the base of human cognition - people who do those memory competitions, the clear winning strategy is the memory palace, or imagining walking through a house where each room is another number - they have to build step by step memory, it's not like an SQL database where they can just SELECT from random. Another one was the insight from GTD that if you remember 7 things to do, you're always repeating those 7 things to yourself to keep them in active memory. There's a strong argument that the mental model is derivative of a predictive model in the human brain, and we can just appear to have a mental model since we have an internal dialogue that runs so fast in the background that even we rarely recognize it. (anyone who has kept a steady meditation or similar practice should be familiar with it)

ffwd 3y ago
Well I can't say any of this for sure but I want to say upfront, I think llm's can in theory do a lot (not sure if most) of the computations a human can do, but it's important to realize, imo, it's not actually stepping through the steps in the way humans are. When we give complicated step by step prompts and so on, it only means it's creating new constraints for what the probability of the next token is (from what exists in the data/model). If the data/model doesn't contain the data needed to produce the desired result, or the data that it was trained does not have examples that can generalize (but not be specific to) the desired result then it can't produce it. That's the difference between humans and llm's imo. We can generalize any "computation" we have to any other "desired output" we want, by thinking about it, while llm's aren't at least not now, so general that they use low level representations of all the 'objects' we can prompt about. Like humans can reason about the objects and things in our mind almost infinitely and recursively while also retaining all the physical realities and facts of those objects, while an llm is limited in this regard. Doesn't mean it can't in theory, there is some weird generalization going on as far as I can tell, but it feels like it's going to need a lot more data or something to do it.