ML is not memorization. Besides, how much memory do you think this model has?

I know, it's worse.

It's understanding.

LLMs neither understand nor reason, that has been shown multiple times.

The bar for this excludes humans xor includes LLMs. I guess you're opting for the former?

If you don't believe me, here is a fun mental exercise: define "understand" and "reason" in a measurable way, that includes humans but excludes LLMs.

It's pretty easy to craft a prompt that will force the LLM to reply with something like

> The `foobar` is also incorrect. It should be a valid frobozz, but it currently points to `ABC`, which is not a valid frobozz format. It should be something like `ABC`.

Where the two `ABC`s are the exact same string of tokens.

Obviously nonsense to any human, but a valid LLM output for any LLM.

This is just one example. Once you start using LLMs as tools instead of virtual pets you'll find lots more similar.

People say nonsense all the time. LLMs also don't have this issue all the time. They are also often right instead of saying things like this. If this reply was meant to be a demonstration of LLMs not having human level understanding and reasoning, I'm not convinced.

Not people, some people.

But a LLM is sometimes the genius and sometimes the idiot.

That doesn’t happen often if you always talk to the same person

"Doing 'A' is wrong, to fix it you need to do 'A'" is not an idiotic statement. It's complete non-sense that no human has ever uttered.

But for LLMs these kinds of illogical statements are common.

LLMs don't think; what they do cannot be in good faith called "thinking" by any definition.

Abstractly, humans function in the same fashion. You could then say the same about us.

Seems like some don’t like that LLMs aren’t really intelligent.

https://neurosciencenews.com/llm-ai-logic-27987/

The study tested transformers, not LLMs.

They trained models on only task specific data, not on a general dataset and certainly not on the enormous datasets frontier models are trained on.

"Our training sets consist of 2.9M sequences (120M tokens) for shortest paths; 31M sequences (1.7B tokens) for noisy shortest paths; and 91M sequences (4.7B tokens) for random walks. We train two types of transformers [38] from scratch using next-token prediction for each dataset: an 89.3M parameter model consisting of 12 layers, 768 hidden dimensions, and 12 heads; and a 1.5B parameter model consisting of 48 layers, 1600 hidden dimensions, and 25 heads."

We're at the peak of the hype cycle right now.

Ask these questions again in two years when the next winter happens.

Or, ignore the hype, look at what we know about how these models work and about the structures their weights represent, and base your answers on that today.

That’s what my link is for

Sure, if you still think the word has meaning.

Yes, I do. Any way you slice this term, it looks close to what ML models are learning through training.

I'd go as far as saying LLMs are meaning made incarnate - that huge tensor of floats represents a stupidly high-dimensional latent space, which encodes semantic similarity of every token, and combinations of tokens (up to a limit). That's as close as reifying the meaning of "meaning" itself as we ever come.

(It's funny that we got there through brute force instead of developing philosophy, and it's also nice that we get a computational artifact out of it that we can poke and study, instead of incomprehensible and mostly bogus theories.)

ML is a kind of memorization, though.

Anything can be a kind of something since that's subjective...

But it’s more kind of memorization than understanding and reasoning

Yea, anything can be a kind of something else. :clown:

Bruh. Do you need to be paid to interact in good faith or were you raised to be social