alt Hacker NewsI couldn't agree with you more.
I really do find it puzzling so many on HN are convinced LLM's reason or think and continue to entertain this line of reasoning. At the same time also somehow knowing what precisely the brain/mind does and constantly using CS language to provide correspondences where there are none. 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.
As I'm on the subject LLM's don't hallucinate. They output text and when that text is measured and judged by a human to be 'correct' then it is. LLM's 'hallucinate' because that is literally what they can ONLY do, provide some output given some input. They don't actually understand anything about what they output. It's just text.
My paper and pen version of the latest LLM (quite a large bit of paper and certainly a lot of ink I might add) will do the same thing as the latest SOTA LLM. 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.
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I have had conversations at work, with people who I have reason to believe are smart and critical, in which they made the claim that humans and AI basically learn in the same way. My response to them, as to anyone that makes this claim, is that the amount of data ingested by someone with severe sensory dysfunction of one sort or another is very small. Helen Keller is the obvious extreme example, but even a person who is simply blind is limited to the bandwidth of their hearing.
And yet, nobody would argue that a blind person is any less intelligent that a sighted person. And so the amount of data a human ingests is not correlated with intelligence. Intelligence is something else.
When LLMs were first proposed as useful tools for examining data and proving answers to questions, I wondered to myself how they would solve the problem of there being no a-priori knowledge of truth in the models. How they would find a way of sifting their terabytes of training data so that the models learnt only true things.
Imagine my surprise that not only did they not attempt to do this, but most people did not appear to understand that this was a fundamental and unsolvable problem at the heart of every LLM that exists anywhere. That LLMs, without this knowledge, are just random answer generators. Many, many years ago I wrote a fun little Markov-chain generator I called "Talkback", that you could feed a short story to and then have a chat with. It enjoyed brief popularity at the University I attended, you could ask it questions and it would sort-of answer. Nobody, least of all myself, imagined that the essential unachievable idea - "feed in enough text and it'll become human" - would actually be a real idea in real people's heads.
This part of your answer though;
"My paper and pen version of the latest LLM .... My paper and pen version of the latest LLM"
Is just a variation of the Chinese Room argument, and I don't think it holds water by itself. It's not that it's just an algorithm, it's that learning anything usefully correct from the entire corpus of human literary output by itself is fundamentally impossible.
People believe that because they are financially invested in it. Everyone has known LLMs are bullshit for years now.
> 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.