There’s been some success in locating abstract concepts in LLMs, and it’s generally clear that their reasoning is mainly operating over “shallow” patterns. They don’t keep track of precise details of scenes. They’re thinking about e. g. narrative tropes, not low-level details.
Granted, that’s the abstraction level at which simulacra themselves are modeled, not distributions-of-simulacra. But that already suggests that LLMs are “efficient” simulators, and if so, why would higher-level reasoning be implemented using a different mechanism?
Think about how you reason, and what are more and less efficient ways to do that. Like figuring out how to convince someone of something. A detailed, immersive step-by-step simulation isn’t it; babble-and-prune isn’t it. You start at a highly-abstract level, then drill down, making active choices all the way with regards to what pieces need more or less optimizing.
Abstract considerations with regards to computational efficiency. The above just seems like a much more efficient way to run “simulations” than the brute-force way.
This just seems like a better mechanical way to think about it. Same way we decided to think of LLMs as about “simulators”, I guess.
Isn’t physics a counterexample to this?
No? Physics is a dumb simulation just hitting “next step”, which has no idea about the higher-level abstract patterns that emerge from its simple rules. It’s wasteful, it’s not operating under resource constraints to predict its next step most efficiently, it’s not trying to predict a specific scenario, etc.
Nothing decisive one way or another, of course.
There’s been some success in locating abstract concepts in LLMs, and it’s generally clear that their reasoning is mainly operating over “shallow” patterns. They don’t keep track of precise details of scenes. They’re thinking about e. g. narrative tropes, not low-level details.
Granted, that’s the abstraction level at which simulacra themselves are modeled, not distributions-of-simulacra. But that already suggests that LLMs are “efficient” simulators, and if so, why would higher-level reasoning be implemented using a different mechanism?
Think about how you reason, and what are more and less efficient ways to do that. Like figuring out how to convince someone of something. A detailed, immersive step-by-step simulation isn’t it; babble-and-prune isn’t it. You start at a highly-abstract level, then drill down, making active choices all the way with regards to what pieces need more or less optimizing.
Abstract considerations with regards to computational efficiency. The above just seems like a much more efficient way to run “simulations” than the brute-force way.
This just seems like a better mechanical way to think about it. Same way we decided to think of LLMs as about “simulators”, I guess.
No? Physics is a dumb simulation just hitting “next step”, which has no idea about the higher-level abstract patterns that emerge from its simple rules. It’s wasteful, it’s not operating under resource constraints to predict its next step most efficiently, it’s not trying to predict a specific scenario, etc.