> it’s trivial to show that humans do not need language to do mental tasks
LLMs don't need language to do mental tasks, either. Their input and output is language - like humans - but in between, the high-dimensional vector representations (often loosely called latent space) are not language in any meaningful sense.
LLMs can benefit from "thinking out loud" much as humans can. The issue is not whether the supposed "thoughts" are actually representative on any "internal" thoughts, but rather that explicating the problem in more detail can help reach better conclusions.
One point I was making is that the idea that humans are doing something "special" (or in the OP comment's terms, "real") in this area isn't well-supported, in fact there's plenty of evidence against it.
> LLMs can benefit from "thinking out loud" much as humans can.
The two processes aren't equivalent. An LLM that fills the thinking trace with a meaningless placeholder token will still exhibit improved performance. There are also regularly things in the thinking trace that don't match the final output if you look closely but on the surface they appear convincing.
It's largely a trained performance. If you go in with the erroneous expectation that it accurately reflects the underlying thought process then you're likely to come away with faulty conclusions.
My point is that language is not a requirement for humans to perform mental tasks absolutely. It is a fundamental requirement of a large language model.
That's a meaningless argument of definition. Replace the language input and output with something else and it's no longer termed an LLM. It's like saying that a "human who writes with right hand" fundamentally requires his right hand in order to write anything because without it he is no longer a "human who writes with right hand" despite that he is still writing (now with his left hand).
LLMs don't need language to do mental tasks, either. Their input and output is language - like humans - but in between, the high-dimensional vector representations (often loosely called latent space) are not language in any meaningful sense.
LLMs can benefit from "thinking out loud" much as humans can. The issue is not whether the supposed "thoughts" are actually representative on any "internal" thoughts, but rather that explicating the problem in more detail can help reach better conclusions.
One point I was making is that the idea that humans are doing something "special" (or in the OP comment's terms, "real") in this area isn't well-supported, in fact there's plenty of evidence against it.