JohnMakin
today at 6:50 PM
They arenât human, donât think like humans, arenât remotely comparable to the way humans think and act, so why would you make this as a 1:1 comparison? This kind of framing is really weird to me.
Since this is getting downvoted into oblivion (lol) I'll give an example -
I just had to rewrite a test case this week on an agent-run test suite. One test was to produce a file of 273 'a' characters as its name.
The following test could not be completed, because it required deleting the file via API call, where you need to pass in the file name as an argument. It could not reliably, and hardly ever, get the correct file name. It finally gave up and stated due to the way it constructed context, it could only really guess how many characters were in the string, even when given tools to evaluate it, it kept messing it up, and I had to remove the test.
Tell me how "human" that is. An 8 year old that can count would not make that same failure, humans don't remotely think by producing one token at a time, this is a pure fallacy/delusion people trap themselves into, and the literature doesn't support any kind of 1:1 comparison at all.
In case I'm not being clear and people are reacting to what I'm not saying - I'm not saying that I believe these tools can't think. I'm saying they don't think like humans do. There is no evidence for that whatsoever in any field anywhere. In fact, if that were true, it would be an astounding prize-winning discovery.
And you don't even want these to think like humans. Humans are dumb and easily replaceable by other humans. What is the point of making a machine human? You want this to be smarter than humans, not think like them. It's all just such nonsense to me, this whole line of thinking.
It turns out that picking up tone isn't a purely human thing and hasn't been for a while. Your Google search term is "sentiment analysis". It predates LLMs.
However, LLMs are fantastic at it. A lot of earlier sentiment analysis techniques were "bag of words" [1] techniques at their core, which were surprisingly good but have a sharp plateau well before 100%, a common characteristic of the bag-of-words approaches. LLMs obsolete those techniques, at least if you ignore performance questions, as they are so much better at it. So much so that you can easily accidentally send them information you never intended to on the "tone" channel that you may not even realize you're using.
[1]: https://en.wikipedia.org/wiki/Bag-of-words_model
People say LLMs are just fancy autocorrect, but they are actually just fancy dungeon and dragons players, if you tell them they are a wizard they will do their best to act like a human playing a wizard, if you tell them their job is on the line they do their best to pretend like they are a human whose job is on the line.
It's all just roleplay.
And yet they're trained on the corpus of human writing. They may not act like humans but they do act like human writing.
"If you don't make profit, your business will be closed" is a pretty clear ultimatum for an agent tasked with creating a profitable business.
logicchains
today at 7:03 PM
You can literally read their thoughts if you run an open model, they look like pretty human thoughts to me, albeit a neurotic human.
JohnMakin
today at 7:47 PM
These aren't thoughts how humans literally think them.
I can write a program to produce a string that looks like human thinking, is it human thinking? Of course it isn't. It's such a silly comparison.
infinite_spin
today at 8:57 PM
> aren't remotely comparable to the way humans think and act
Neural networks in machine learning/AI are comparable to neural networks in human brains. What made you think they aren't?