Comment by samrus
15 hours ago
I like the idea of more reccurance in the transformer level. Chain of thought always seemed so clunky. Its just not the way the human brain processes information. Its an extrmeely crude approximation at best
15 hours ago
I like the idea of more reccurance in the transformer level. Chain of thought always seemed so clunky. Its just not the way the human brain processes information. Its an extrmeely crude approximation at best
it is what I do to solve hard problems through.
easy stuff happens by itself, but with a system large enough you need a scratchpad and a rubber duck.
One thing about the reasoning is that models are trained to generate a chain of thoughts, but it doesn’t have to be correct, accurate, or reflect the underlying logic of the LLM. It’s the same problem we have with the output, it is something plausible, but not that reliable
>but it doesn’t have to be correct, accurate, or ...
why we don't do GAN here, ie. second model verifying correctness/accuracy/etc. ?
I do the same thing in my head. There is no underlying logic to an llm. Logic is an external construct alien to human like forms of reasoning.
Yes, both the output should be "milestones" of sorts, like lemmas and theorems in math. Important plateaus that serve as a launching pad to the next phase. Regurgitating every thought potentially degrades signal:noise ratio.
The hidden states of the tokens likely contain more semantic information than can be extracted by the final projection into token space.
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Chain of thought seems very specifically like an instantiation of the System 2 thinking of the System 1 and 2 thinking Daniel Kahneman popularized.
Is it clunky in that it's a verbalized/languified version of system 2 thinking, and clearly humans do some non-verbal version too?
Some humans apparently only or almost only do non-verbal thinking. It’s called anendophasia.
Sounds like philosophical zombies.
https://en.wikipedia.org/wiki/Philosophical_zombie
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What I'd like to see is "brain"-machine interface for LLMs that plugs things like calculator and other modules, directly into the neural network instead of accessing them through in-context tool calls.
Why? I talk in my head and then enunciate only that which is relevant. My speech rate inside is incredibly fast.
The purpose of the scratch pad is very often to refer back to it later.