Modelling text describing the world is not modelling (some aspect) of the world?
Modelling the probability that a reader likes or dislike a piece of text is not modelling (some aspect) of a reader's state of mind?
I don't understand this point. I feel like almost everything associated with computing is extruding synthetic text.
That's captured elsewhere - attempts to create "synthetic human behavior" - but mostly around ethics vs practical function or consumer appeal.
Even just a "stochastic parrot" can be extremely valuable if the parrot is fast enough and can connect enough dots in a human-reasoning-style to say things like "what could come after a description of a problem, some background info, and a question about what could have caused the problem? Probably a relevant hypothesis that fits the background facts and the problem description" and then generate a high-probability-fitting sequence of text to spit out.
There doesn't need to be any more intent in that than just "predict what would be the next text that would be similarly connected to the previous in the same way text in the model training process would." It doesn't need to be intending to solve the problem if the hit rate is good enough such that predicting how someone else would describe the solution is often the same as actually "intending" to solve it...
Nor does the ability to predict things stochasticly mean that there isn't any symbolic way to do the same. Quite possibly the stochastic process is just a brute-force rough approximation of what a true symbolic model could do. IMO the success of the stochastic approach is exactly in line with the existence of some sort of underlying structure/system. (Though such as system would have to be incredibly complex to support all the crazy things we do with language.)
isn't that circular reasoning?
"I can call anyone not smart enough to take offense because as I said those anyone aren't smart enough to take offense"?
(also disregarding that being offended has been shifted into "protection of the (perceived) weak (or of the group of your allegiance)" rather than "protection of self" for quite some time now)
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but generally I always felt that this tension around the phrase was somewhat of perscriptive/descriptive difference, or maybe "level of detail in the model" type
just because there is knowledge of a more full understanding of the process doesn't mean other descriptions/modeling of the process are invalid or unuseful
newtonian gravity doesn't describe time dilation - and yet most of the time it is enough to use only it, so it's successfully studied in schools and undergrads
if output of LLM can be modeled (by intuition) as "some other being" for many practical uses *and model works* - then automatical blaming others for "using less precise model" and warning about it feels... strange
Maybe that's the best one can do when describing something very new and strange. A series of vivid, incompatible metaphors might be the best guide for a while. "Intelligence" as we normally understand it is a significant overstatement, while "parrot" is a massive understatement.
Meanwhile you have multiple Fields Medalists (Tau, Gowers) saying they’re very impressed by LLMs’ mathematical reasoning, something that the stochastic parrots thesis (if it has any empirically-predictive content at all) would predict was impossible. I doubt Tau and Gowers thought much of LLMs a few years ago either. But they changed their minds. Who do you want to listen to?
I think it’s time to retire the Stochastic Parrots metaphor. A few years ago a lot of us didn’t think LLMs would ever be capable of doing what they can do now. I certainly didn’t. But new methods of training (RLVR) changed the game and took LLMs far beyond just reducing cross entropy on huge corpuses of text. And so we changed our opinions. Shame Emily Bender hasn’t too.
Sigh.
Renaissance Philanthropies is a front for VC companies.
They never publish allocated computational resources, prior art or any novel algorithm that is used in the LLMs. For all we know, all accounts that are known to work on math stunts get 20% of total compute.
In other words, they ignore prior art, do not investigate and just celebrate if they get a vibe math result. It isn't science, it is a disgrace.
It's rare to read an author who can directly face Brandolini's Law of misinformation asymmetry and not only hold his own against the bullshit but overcome it.
LLMs certainly use something similar, except they understand text as input. LLMs, especially used for marketing stunts, have way more computing power available than any theorem prover ever had. They probably do random restarts if a proof fails which amounts to partially brute forcing.
Lawrence Paulson correctly complained about some of the hype that Lean/LLMs are getting.
ACL2 even uses formulaic text output that describes the proof in human language, despite being all in Common Lisp and not a mythical clanker.
They do not think and use old and well established algorithms or perhaps novel ones that were added.
