This paper introduces a term and instantly defines it as a definitely biased thing that is definitely happening, then spends its entirety arguing against the strawman it built itself. Not a single sentence is spent actually arguing with the idea or any of its points (other than the “partial similarities” paragraph on page I just realized the pages aren’t even numbered).
In general, the terms “LLM-like” and “human-like” are used all over the place, and in contrast with each other, but they’re never actually defined. It all just seems more vibes-based than anything else.
And “treating the human cognitive process like it’s similar to the LLM cognitive process might lead to a society where epistemics turns into a discipline where plausibility is an acceptable substitute for empiricism” has got to be one of the most ridiculous notions I’ve ever read in a paper (ctrl+F “fifth pathway is epistemic” for the exact quote).
> When artificial systems produce human-like language, people may draw a reverse inference: if LLMs can speak like humans, perhaps humans think like LLMs.
I think I experienced this when I learned about LLMs, chain of thought, thinking tokens, short-term memory context, and long-term memory context. I began applying these concepts to real life and reasoning about how our brains work as if these concepts described how our brains actually function. But maybe this is more akin to the Tetris effect?
I think it's meaningless anyway. A calculator doesn't multiply numbers like a human does. The important part is to develop systems that can do many human tasks
Agreed. I think we, as humans, like to think in terms of various metaphors when it comes to how we perceive ourselves in the world ( for example, "I am not some sort of automaton/robot" when objecting to some boss way back when ).
This paper introduces a term and instantly defines it as a definitely biased thing that is definitely happening, then spends its entirety arguing against the strawman it built itself. Not a single sentence is spent actually arguing with the idea or any of its points (other than the “partial similarities” paragraph on page I just realized the pages aren’t even numbered).
In general, the terms “LLM-like” and “human-like” are used all over the place, and in contrast with each other, but they’re never actually defined. It all just seems more vibes-based than anything else.
And “treating the human cognitive process like it’s similar to the LLM cognitive process might lead to a society where epistemics turns into a discipline where plausibility is an acceptable substitute for empiricism” has got to be one of the most ridiculous notions I’ve ever read in a paper (ctrl+F “fifth pathway is epistemic” for the exact quote).
> When artificial systems produce human-like language, people may draw a reverse inference: if LLMs can speak like humans, perhaps humans think like LLMs.
I think I experienced this when I learned about LLMs, chain of thought, thinking tokens, short-term memory context, and long-term memory context. I began applying these concepts to real life and reasoning about how our brains work as if these concepts described how our brains actually function. But maybe this is more akin to the Tetris effect?
I teach students to use their own imagination like generative AI. Prompting works. They just need a bit of practice.
I think it's meaningless anyway. A calculator doesn't multiply numbers like a human does. The important part is to develop systems that can do many human tasks
> are [we] beginning to attribute too little mind to humans.
I don’t think this way of thinking started with LLM. Does Systems Based Thinking also attribute too little mind to humans?
Agreed. I think we, as humans, like to think in terms of various metaphors when it comes to how we perceive ourselves in the world ( for example, "I am not some sort of automaton/robot" when objecting to some boss way back when ).
I'm sure we don't know for sure that humans work like LLMs, but do we know that they don't?
Highly relevant: Reading Doesn't Fill a Database, It Trains Your Internal LLM <https://tidbits.com/2026/02/28/reading-doesnt-fill-a-databas...>