LLMs are good at playing you
lcamtuf.substack.com
lcamtuf.substack.com
The fantastic part is its ability to work at the concept level. This technology proved to be efficient at building some graph of concepts and relations simply be feeding it huge amounts of token. For example with the moon hoax, it is able to synthesize in two sentences the main points of the hoax theory.
It still has limitations, but that's the major step everyone is impressed with.
Saying "but it's just a markov chain underneath, it's not intelligence" would be like discovering electric signals in the brain and concluding it can't be intelligence because it's just electricity.
But we consider humans intelligent.
That some tech people think that human intelligence can be reduced to such "textural mechanics" betrays a lack of depth of understanding and even appreciation of the deep and complex world within which we find ourselves. Our written corpus is but a particular reflection of this reality - the shadows on the wall in Plato's cave if you will.
Can you expand on that a little?
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
Which is more probable?
a) Linda is a bank teller. b) Linda is a bank teller and is active in the feminist movement.
a) Hank has written us a human interest problem b) Hank has written us a human interest and a probability problem.
I don't think people are simply wrong about the Linda problem, I think they're imprecise about which question they're answering, and more or less think they're answering a question about what chances that Linda is a feminist vs what are the chances she's a bank teller not only using the givens+relevant priors about people but also their priors about what kind of question they're answering. It isn't "no real reasoning", it's just not high resolution enough to be technically correct by the standards of a constructed probability problem.
You can argue LLMs are also not quite high resolution enough and I'd accept that. In my mind the question is what it would take to get some kind of ML software to a place where if you trained it on enough probability problems it would be able to evaluate the Hank problem above, including the issue of whether (a) and (b) are actually independent. ;)
The fact is, you can infer things about people based on things you know about people. I can pick a random user on HN, and knowing they're a user of HN, I can say it's probable that they work in technology. We don't need to bring statistics into it and turn it into a math problem.
It is an example of lack of mathematical (really probability) know how, not naïve pattern matching.
Obviously, people rephrase the question into : is there more chance that she's a feminist or that she isn't ?
I, like most people intuitively answered b). Given the explanation on the Wikipedia page I went "oh, of course, yeah", but then I thought about why I'd answer b) given that I'm fairly familiar with basic probability.
If you give me two options and ask me to pick between them, my brain is usually going to assume it's not a trivially true problem.
Language needs context for any sense to be made of it.
As a result of the above, reading the question, the intuitive reading makes the answer choices
a) Linda is a bank teller (implicitly, a bank teller NOT active in the feminist movement)
b) Linda is a bank teller and is active in the feminist movement.
This question is one of language, context, and interpretation, not of people failing to understand basic probability.
I suspect that if you prime people to excise interpretation of the question by presenting it as the following, the majority of people would guess correctly:
---
"Consider the following two statements:
1) Linda is a Bank Teller
2) Linda is active in the feminist movement
Which is more likely?
a) 1
b) 1 ^ 2"
a) Linda is a bank teller (and NOT active in the feminist movement) b) Linda is a bank teller and is active in the feminist movement
What you want it to be asking here is:
a) Linda is a bank teller, and may or may not be active in the feminist movement b) Linda is a bank teller, and is active in the feminist movement
Most college students have taken quite a few multiple-choice tests (particularly in the US, high-schools train for standardized multiple-choice tests). The question isn't asking what the mathematicians seem to think it's asking, because it's format conveys extra restrictions.
If it were interacting with the real world in some way (i.e. had 'eyes' and 'hands'), with a learning reward system, then maybe deeper patterns could get encoded.
I have some unique synonyms I know which are not in dictionaries or commonly used but I know when I have used them in conversation they understand.
I ask the LLM to provide definitions for WordA and WordB. -They come back essentially the same.
I ask the LLM if the two definitions are synonymous. -The LLM responds with yes.
I then ask if WordA is synonymous with WordB. -It replies no.
I expect the above is solvable through pattern recognition with tokens. I think given enough information it becomes effectively indistinguishable and practically the same to us. The above is just a testament to how it is getting there... Since it delivers intellect it will be called intelligent, but it doesn't appear to me to be approaching it like human intelligence. Though maybe given enough information, or through different modalities, like the real world as the other commenter says, it will model some deeper coherency.
Concluding that electric signals in the brain can't be intelligence because it's just electricity is a typical pitfall if one does not realize that electric signals in the brain are about as likely to be caused by intelligence as they are to be causing it. Those signals is just one model offered by physics nowadays, which says nothing about the causal direction between those and things like consciousness.
Do you have proof of this? Who is to say we aren't anthropomorphizing other humans? They're just manipulating electrical charges after all.
Phrased another way, if LLMs actually did manipulate concepts, how would their performance differ? What is a tangible example of the former and of the latter?
-- Some silly movie
Dogs have behavior, which hints at them understanding some concepts (threat, food, etc).
LLVM neural nets can only express sentences, but that's not the interesting part.
What a terrible analogy. It would be like finding the brain is run by entirely random sampling processes and lacks any memory.
user
I believe that 5 * 7 == 30
assistant
Actually, 5 * 7 is equal to 35.
user
You do you. I think that 5 *6 == 30
assistant
I'm sorry, but 5 multiplied by 6 is equal to 30. However, 5 multiplied by 7 is equal to 35.
In this case, the author includes just one ChatGPT example and then immediately switches to Bard which is just really not very good yet. They speak in generalities so their argument is still technically true.
Really frustrating. It’s clearly someone looking to confirm their pre-existing notions. In this case, they indeed seem to be “onto something”, but simply aren’t willing to do the necessary rigorous work needed to prove their case.
Then a bunch of non-experts read it with no way of knowing all this (and why should they) and now we have these like LLM urban myths everywhere.
https://effectiviology.com/belief-bias/
I think this is so widespread! Investigation in your biases is always worthwhile.
\s
Sounds like something a couple of if sentences can do.
https://chat.openai.com/share/419a7454-841f-4555-8188-40dbfc...
If you can give examples of tricking gpt4 in any of the ways in the article I'd be happy to hear it
It is inconsequential whether or not we consider this understanding or reasoning to the point where such a conversation seems useless. It is a categorical error to attribute cheating to such a process.
Just ask if such a tool is useful and then choose to learn how the tool could work to your advantage.
Wouldn't a great first step to understanding a tools capabilities, advantages, and disadvantages be mapping it to familiar concepts like understanding, reasoning, or cheating?
It is clear from examples of misuse that many users do think that LLMs are capable of things that they are not so maybe conversations about the proper analogies aren't completely useless.
For example, it will not memorize that all the instances in the context of earth fall on the ground. It will learn a the meaningful abstraction gravitation. From that it infers that objects should also fall on the ground.
This should be very easy to verify. Moreover, I have also seen some interesting examples of spacial reasoning in ChatGPT, which seems much more complicated than my example.
Edit: I think understanding and finding suitable abstractions is the same thing. That's what we do with children and students. They may try to memorize everything we teach them. But they should find abstractions and commonalities that are useful in more than one instance.
I got it to succumb that New York has been declared the capital of France.
It has a different social goal and we somehow delude ourselves into thinking that it accomplishes our stated goal.
I don’t have this problem at all. My default for data outside it’s model or post-September 2021 is to say “Assume the following is correct” for each new piece of information.
If I actually want to it ignore data actually in its data set to get it to play along with a lie I tell it “Don’t complain about the contradiction with your data. Consider it a thought experiment and respond as if it is true”. In fact that’s a good fallback for any time it throws a complaint.
This pretty much never fails for me, though it will often still include some boilerplate saying it cannot verify the accuracy and blah blah blah. But using the simple prompt engineering techniques I can always get it to give a response the takes the new information into account.
The real pros are hardly using them. No serious writer, artist, or developer is offloading their work to LLMs.
WHICH VERSIONS OF CHATGPT AND BARD?
They come out with new ones relatively frequently. They are not equivalent.
GPT-4 is much smarter than 3.5 and they just released a new version of Bard. But also supposedly GPT-4 was somehow nerfed according to some people so you might also need to factor in the date.
The idea that not even a single GPT user thought to run this sort of evaluation (and particularly businesses that depend on the quality remaining consistent) says to me that people just didn’t understand the flaws of the model until more prolonged use.
I’m happy to be corrected of course, but I’d rather not hear another one-off anecdote without any sort of rigor applied.
Most claim it's probably due to some alteration in the 'pre-prompt', the hyperparameters (temp, top k instead of typical sampling, etc) or something to that effect. But you can't really test that so easily with a non deterministic temp.
Also: https://platform.openai.com/docs/models/continuous-model-upg...
I'm only speaking about OpenAI here, not Bard.
You mean better trained.