Great response! I wonder if part of my disagreement is in thinking of GPT4 as a System rather than just as an LLM. If the system has the ability to solve probl

Great response! I wonder if part of my disagreement is in thinking of GPT4 as a System rather than just as an LLM. If the system has the ability to solve problems in code it feels like intentionally handicapping it by forcing it to solve problems in natural language. So it's not super interesting for me if you say that GPT4 the llm can solve only O(n) problems when GPT4 the system can solve more complex ones. Though I do admit theres an issue where the prompter needs to preemptively identify problems that should be solved in code and that's a significant shortfall. > the promise of scaling is that eventually big data pays off and the LLM gains some sort of generalizable understanding of the problem. But if the performance varies smoothly and predictably, then that's probably not happening - LLMs never "grok" subjects Is this any different from humans? Even a Nobel Prize winning Physicist will make some mistakes, does this indicate they don't grok Physics? >if GPT-3 got a 50% on 3-digit arithmetic because its training data include 30% of all 3-digit arithmetic problems, and GPT-4 got an 80% because its training data includes 50% of all such problems, then that's a vacuous and uninteresting form of scaling Genuinely curious, is there any indication this is actually what's happening? It seems pretty trivial to generate novel questions in other domains for example and GPT4's performance seems to generalize to those questions. I.e. GPT4 can do well on unreleased GRE problems for example. Or do you think GPT4 is actually just memorizing everything and has 0 ability to generalize?