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> This is just a completely base level of understanding of LLMs. How do you predict the next token with superhuman accuracy? Really think about how that is poss

> This is just a completely base level of understanding of LLMs. How do you predict the next token with superhuman accuracy? Really think about how that is possible. If you think it's just stochastic parroting you are ngmi. Yes, thank-you, I do understand how LLMs work. They learn a lot of generative rules from the training data, and will apply them in flexible fashion according to the context patterns they have learnt. You said stochastic parroting, not me. However, we're not discussing whether LLMs can be superhuman at tasks where they had the necessary training - we're discussing whether they are capable of creativity (and presumably not just the trivially obvious case of being able to apply their generative rules in any order - deductive closure, not stochastic parroting in the dumbest sense of that expression).

vonneumannstan 1y ago
>However, we're not discussing whether LLMs can be superhuman at tasks where they had the necessary training - we're discussing whether they are capable of creativity "Even if all the model is capable of is memorizing text then just the sheer volume it has memorized should yield unique insights, no human can ever hope to hold this much text in their memory and then make connections between it." Unless you think humans have magic meat then all we are really doing with "creativity" is connecting previously unconnected facts.
- HarHarVeryFunny 1y ago
  > Unless you think humans have magic meat then all we are really doing with "creativity" is connecting previously unconnected facts. In the case of discovery and invention the "facts" being connected may not be things that were known before. An LLM is bound by it's training set. A human is not limited by what is currently known - they can explore (in directed or undirected fashion), learn, build hierarchies of new knowledge and understanding, etc.
- HarHarVeryFunny 1y ago
  > "Even if all the model is capable of is memorizing text then just the sheer volume it has memorized should yield unique insights, no human can ever hope to hold this much text in their memory and then make connections between it." Yes, potentially, but the model has no curiosity or drive to do this (or anything else) by itself. All an LLM is built to do is predict. The only way to control the output and goad it into using the vast amount of knowledge that it has is by highly specific prompting. Basically it's only going to connect the dots if you tell what dots to connect, in which case it's the human being inventive, not the model. The model is trying to predict, so essentially if you want it to do something outside of the training set you're going to have to prompt it to do that. A human has curiosity (e.g. "what happens if I connect these dots .."), based on prediction failure and associated focus/etc - the innate desire to explore the unknown and therefore potentially learn. The model has none of that - it can't learn and has no curiosity. If the model's predictions are bad it will just hallucinate and generate garbage, perhaps "backtrack" and try again, and likely lead to context rot.

vonneumannstan 1y ago

>However, we're not discussing whether LLMs can be superhuman at tasks where they had the necessary training - we're discussing whether they are capable of creativity "Even if all the model is capable of is memorizing text then just the sheer volume it has memorized should yield unique insights, no human can ever hope to hold this much text in their memory and then make connections between it." Unless you think humans have magic meat then all we are really doing with "creativity" is connecting previously unconnected facts.

HarHarVeryFunny 1y ago
> Unless you think humans have magic meat then all we are really doing with "creativity" is connecting previously unconnected facts. In the case of discovery and invention the "facts" being connected may not be things that were known before. An LLM is bound by it's training set. A human is not limited by what is currently known - they can explore (in directed or undirected fashion), learn, build hierarchies of new knowledge and understanding, etc.
HarHarVeryFunny 1y ago
> "Even if all the model is capable of is memorizing text then just the sheer volume it has memorized should yield unique insights, no human can ever hope to hold this much text in their memory and then make connections between it." Yes, potentially, but the model has no curiosity or drive to do this (or anything else) by itself. All an LLM is built to do is predict. The only way to control the output and goad it into using the vast amount of knowledge that it has is by highly specific prompting. Basically it's only going to connect the dots if you tell what dots to connect, in which case it's the human being inventive, not the model. The model is trying to predict, so essentially if you want it to do something outside of the training set you're going to have to prompt it to do that. A human has curiosity (e.g. "what happens if I connect these dots .."), based on prediction failure and associated focus/etc - the innate desire to explore the unknown and therefore potentially learn. The model has none of that - it can't learn and has no curiosity. If the model's predictions are bad it will just hallucinate and generate garbage, perhaps "backtrack" and try again, and likely lead to context rot.