Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.
I both agree and disagree. I think of them as golems. They do understand how to respond, but that's as deep as it goes. It's simulated understanding, but a very very good simulation... Okay maybe I do agree.
I think that at best you could say that they understand the relationship between tokens. But even that requires a really generous definition of the word "understand".
it's almost like this thing has no internal conceptual representation! I know this can't possibly be, millions of promptfans and prompfondlers have told me it can't be so, but it sure does look that way! wild!
It must have some internal models of some things, or else it wouldn't be possible to consistently make coherent and mostly reasonable statements. But the fact that it has a reasonable model of things like grammar and conversation doesn't imply that it has a good model of literally anything else, which is unlike a human for whom a basic set of cognitive skills is presumably transferable. Still, the success of LLMs in their actual language-modeling objective is a promising indication that it's feasible for a ML model to learn complex abstractions.
This article is over a year old and you all seem to be buying it as relevant to the current state of things. Can anyone reproduce the experiments/conversations where it fumbles with double negatives etc? I tried a couple examples with chatgpt and it seemed to handle them fine
we don’t care that your instance of a nondeterministic, unreliable system can’t replicate someone else’s results, and we don’t take marching orders from SSC readers.