It would have been nice to see some version of “I am very surprised by how far LLMs have come since I wrote the stochastic parrots paper, here is how I have revised my thinking.” But there is nothing like that and the author is just doubling down or trying to correct perceived “misinterpretations” of her work.
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.
Gowers, Tao and Lichtman are especially impressed by the funding of math.inc and the AI for Math Fund, a joint venture of Renaissance Philanthropies and XTX Markets.
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.
Lovely article well worth attention by virtue of its regard for the cultural traits of terminology and its inflections, while also debunking the pervasive lore that "AI" devices are doing anything but the merest resemblance of thinking.
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.
Automated theorem provers are not new, in fact they are very old. One of the most automated is ACL2, which uses the well studied waterfall method (unrelated to waterfall development).
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.
This is the first submission since a year that gives me some hope for humanity. It shows that linguistics is not obsolete. Maybe the last people capable of thinking will be linguists.
"Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind."
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?
What a hill to die on.
It would have been nice to see some version of “I am very surprised by how far LLMs have come since I wrote the stochastic parrots paper, here is how I have revised my thinking.” But there is nothing like that and the author is just doubling down or trying to correct perceived “misinterpretations” of her work.
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.
Gowers, Tao and Lichtman are especially impressed by the funding of math.inc and the AI for Math Fund, a joint venture of Renaissance Philanthropies and XTX Markets.
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.
Lovely article well worth attention by virtue of its regard for the cultural traits of terminology and its inflections, while also debunking the pervasive lore that "AI" devices are doing anything but the merest resemblance of thinking.
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.
TIL that the "merest resemblance of thinking" is enough to take gold at IMO.
Automated theorem provers are not new, in fact they are very old. One of the most automated is ACL2, which uses the well studied waterfall method (unrelated to waterfall development).
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.
And also create novel math proofs.
This is the first submission since a year that gives me some hope for humanity. It shows that linguistics is not obsolete. Maybe the last people capable of thinking will be linguists.