> The simplest example being that LLM's somehow function in a similar fashion to human brains. They categorically do not. I do not have most all of human literary output in my head and yet I can coherently write this sentence.

The ratio of cognition to knowledge is much higher in humans that LLMs. That is for sure. It is improving in LLMs, particularly small distillations of large models.

A lot of where the discussion gets hung up on is just words. I just used "knowledge" to mean ability to recall and recite a wide range of fasts. And "cognition" to mean the ability to generalize, notice novel patterns and execute algorithms.

> They don't actually understand anything about what they output. It's just text.

In the case of number multiplication, a bunch of papers have shown that the correct algorithm for the first and last digits of the number are embedded into the model weights. I think that counts as "understanding"; most humans I have talked to do not have that understanding of numbers.

> It's just an algorithm.

> I am surprised so many in the HN community have so quickly taken to assuming as fact that LLM's think or reason. Even anthropomorphising LLM's to this end.

I don't think something being an algorithm means it can't reason, know or understand. I can come up with perfectly rigorous definitions of those words that wouldn't be objectionable to almost anyone from 2010, but would be passed by current LLMs.

I have found anthropomorphizing LLMs to be a reasonably practical way to leverage the human skill of empathy to predict LLM performance. Treating them solely as text predictors doesn't offer any similar prediction; it is simply too complex to fit into a human mind. Paying a lot of attention to benchmarks, papers, and personal experimentation can give you enough data to make predictions from data, but it is limited to current models, is a lot of work, and isn't much more accurate than anthropomorphization.